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New progresses in swarm intelligence-based computation
Abstract
Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.
... These new algorithms include the artificial fish [17], the artificial bee colony [18,19], the firefly algorithm [20,21] and the cuckoo search [22]. Recognizing that each optimizer and its extensions have distinct characteristics and the performances differ, it wasn't until recently researchers took the effort to investigate how the algorithmic implementations may correlate with the performances [4,[23][24][25][26]. The first comprehensive review on multiple swarm intelligence algorithms may be from Parpinelli and Lopes in which 10 newly developed algorithms were studied [4]. ...
... The authors discussed how the exploitation, exploration, and communication mechanisms from the bioinspirations are implemented in different algorithms. A second notable effort is from [23] in which taxonomy such as control parameters and operators is defined and used to categorize the algorithms. It concludes that the investigation on internal principles of swarm intelligence is of great importance for the development and improvement of the algorithms. ...
... In terms of application, SI algorithms have been applied to continuous optimization, discrete optimization, multi-objective optimization, and constraint optimization. Application of SI algorithms to large-scale optimization, dynamic optimization and complex optimization [23,[100][101][102][103][104][105][106] should be one of the next focuses. ...
Due to the efficiency and efficacy in performance to tackle complex optimization problems, swarm intelligence (SI) optimizers, newly emerged as nature-inspired algorithms, have gained great interest from researchers over different fields. A large number of SI optimizers and their extensions have been developed, which drives the need to comprehensively review the characteristics of each algorithm. Hence, a generalized framework laid upon the fundamental principles from which SI optimizers are developed is crucial. This research takes a multidisciplinary view by exploring research motivations from biology, psychology, computing and engineering. A learning–interaction–diversification (LID) framework is proposed where learning is to understand the individual behavior, interaction is to describe the swarm behavior, and diversification is to control the population performance. With the LID framework, 22 state-of-the-art SI algorithms are characterized, and nine representative ones are selected to review in detail. To investigate the relationships between LID properties and algorithmic performance, LID-driven experiments using benchmark functions and real-world problems are conducted. Comparisons and discussions on learning behaviors, interaction relations and diversity control are given. Insights of the LID framework and challenges are also discussed for future research directions.
... Inspired by nature, modern metaheuristic algorithms have been developed and applied to deal with these complicated problems. Some well-known methods in this context are particle swarm optimization (PSO) (Kennedy and Eberhart 1995;Wang et al. 2014b;Sun et al. 2004;Adewumi and Arasomwan 2016), monarch butterfly optimization (MBO) (Feng et al. 2015;Wang et al. 2015g, h, 2016j;Feng et al. 2017), earthworm optimization algorithm (EWA) (Wang et al. 2015b), artificial bee colony (ABC) (Karaboga and Basturk 2007), ant colony optimization (ACO) (Dorigo and Stutzle 2004), elephant herding optimization (EHO) (Wang et al. 2015c(Wang et al. , 2016d, differential evolution (DE) (Storn and Price 1997;Xu et al. 2016;Wang et al. 2012a), firefly algorithm (FA) (Gandomi et al. 2011;Wang et al. 2014aWang et al. , 2012bYang 2010a;Gálvez and Iglesias 2016;Nasiri and Meybodi 2016), simulated annealing (SA) (Kirkpatrick et al. 1983), intelligent water drop (IWD) algorithm (Shah-Hosseini 2009), monkey algorithm (MA) (Zhao and Tang 2008), genetic algorithm (GA) (Goldberg 1998), biogeography-based optimization (BBO) (Simon 2008;Mirjalili et al. 2014;Duan et al. 2012;Wang et al. 2012c), evolutionary strategy (ES) (Beyer 2001), krill herd (KH) (Gandomi and Alavi 2012), water cycle algorithm (WCA) (Eskandar et al. 2012), cuckoo search (CS) (Yang et al. 2009;Li et al. 2013;Li and Yin 2015;Wang et al. 2012d, free search (FS) (Penev and Littlefair 2005), probability-based incremental learning (PBIL) (Shumeet 1994), moth search (MS) algorithm (Wang 2016), dragonfly algorithm (DA) (Mirjalili 2016), interior search algorithm (ISA) (Gandomi 2014), bat algorithm (BA) (Gandomi et al. 2013c;Yang and Gandomi 2012;Mirjalili et al. 2013;Yang 2010b;Cai et al. 2016;Wang et al. 2015aWang et al. , 2016b, chicken swarm optimization (CSO) (Meng et al. 2014), fireworks algorithm (FWA) (Tan 2015), brain storm optimization (BSO) (Shi 2011;Shi et al. 2013), harmony search (HS) (Geem et al. 2001;Wang et al. 2013a;Niknam and Fard 2016;Rezoug and Boughaci 2016), and stud GA (SGA) (Khatib and Fleming 1998). ...
... After studying the herding behavior of the krill in seas, Gandomi and Alavi (2012) proposed a new swarm intelligence-based (Duan and Luo 2015) global optimization algorithm, called krill herd (KH). The whole optimization process in KH can be divided into three movements. ...
... Twelve chaotic maps are used to tune the inertia weights (ω n , ω f ) used in KH on fourteen benchmarks. The best chaotic map (Singer map) is selected to generate the chaotic KH (CKH) algorithm (Wang et al. 2014c), and it is further compared with other eight state-of-the-art metaheuristic algorithms [ACO (Dorigo and Stutzle 2004), BA (Yang 2010b), CS (Yang et al. 2009 ...
Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.
... GABC, the gbest-guide ABC algorithm was introduced by Zhu and Kwong [13] that integrates information of global preeminent solution and solution search equation to develop the exploitation. Researchers verified that the HGABC algorithm has the prevailing capability of probing universal optimal solution to standard optimization algorithm, GABC. ...
... Karim et al. [12] stated that, there is a great inclination in use of data mining and machine learning methodologies For example, (SVM) support vector machine, neural network (NN), and cluster analysis. Moreover, there is also an improved concern on detecting DDoS detection by a distributed mode [3,13]. In précis, the recent tendency of DDoS detection takes into account an increase of traffic and the insight that can be produced by the availability of data. ...
Intellectual intrusion detection system can merely be build if there is accessibility to an effectual data set. A high dimensional quality dataset that imitates the real time traffic facilitates training and testing an intrusion detection system. Since it is complex to scrutinize and extort knowledge from high-dimensional data, it is identified that feature selection is a preprocessing phase during attack defense. It increases the classification accuracy and reduces computational complexity by extracting important features from original data. Optimization schemes can be utilized on the dataset for selecting the features to find the appropriate subspace of features while preserving ample accuracy rate characterized by the inventive feature set. This paper aims at implementing the hybrid algorithm, ABC-LVQ. The bio-inspired algorithm, Artificial Bee Colony (ABC) is adapted to lessen the amount of features to build a dataset on which a supervised classification algorithm, Linear Vector Quantization (LVQ) is applied, thus achieving highest classification accuracy compared to k-NN and LVQ. The NSL-KDD dataset is scrutinized to learn the efficiency of the proposed algorithm in identifying the abnormalities in traffic samples within a specific network.
... Swarm Intelligence (SI) algorithm and evolutionary methods are emerging evolutionary computing technology, and it has attracted more and more researchers' attention [8]. SI simulates various swarm behaviors of social insects and uses the information interaction and cooperation between individuals in the group to achieve optimization [9]. In recent years, some SI algorithms have been designed by researchers, for example, particle swarm optimization (PSO) [10], genetic algorithm (GA) [11], ant colony optimization (ACO) [11], moth-flame optimization algorithm (MFO) [11], sine cosine algorithm (SCA) [11], Harris hawks optimization (HHO) 1 [12], Slime mould algorithm (SMA) 2 [13], hunger games search 3 (HGS) [14], Runge Kutta optimizer (RUN) 4 [15], colony predation algorithm (CPA) [16], and weighted mean of vectors (INFO) 5 [17]. ...
... Therefore, levy flight is also introduced as a method to explore new solutions, and the approach is defined as Eq. (9). ...
The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the combinatorial optimization problem, which effectively combines the memetic algorithm based on a memetic algorithm and the particle swarm algorithm based on population behavior. The algorithm is widely used because it is easy to implement and requires few parameters to be adjusted. However, there are still some characteristics of this method that need to be improved because it is easy to fall into local optimization or poor search ability. To alleviate this limitation, a new version of the improved SFLA is proposed in this paper, which incorporates a dynamic step size adjustment strategy based on historical information, a specular reflection learning mechanism, and a simulated annealing mechanism based on chaotic mapping and levy flight. Firstly, the dynamic step size adjustment strategy based on historical information effectively helps to balance local exploration and global exploitation and alleviates the problem of falling into local optimum. Second, the specular reflection learning mechanism increases the possibility of searching for valid solutions in feasible domains and enhances the search ability of individuals in the population. Finally, an improved simulated annealing strategy is executed for each memetic, which improves the efficiency of local exploitation. In order to test the performance of the proposed algorithm, 31 test functions were selected from IEEE CEC2014 and 23 essential benchmark functions, and comparative experiments were carried out from the two dimensions of 30 and 100. Furthermore, a series of competing algorithms are selected, which involve nine classical standard algorithms, including PSO, BA, SSA, FA, SCA, WOA, GWO, MFO, and SFLA, as well as, six well-known improved algorithms, including LSFLA, DDSFLA, GOTLBO, ALCPSO, BLPSO, CLPSO. Furthermore, Wilcoxon signed-rank test and Friedman test are used as testing tools to illustrate the scalability of the proposed algorithm. From the analysis of the results, it can be seen that this proposed method has been effectively improved concerning stability and the quality of the optimal solution obtained from the search, both in low and high dimensions, and the ability to jump out of the local optimum has also been ameliorated. In addition, to prove that this method has a reliable performance in discrete problems and continuous problems, DSSRLFLA is mapped into a discrete space, and 24 UCI data sets are also selected to evaluate the performance of the new feature selection method. The experimental results illustrate that this improved method can obtain fewer features and higher classification accuracy than some well-known feature selection methods.
... This characteristic is desirable to handle the challenge of increasingly complex optimization problem; as a result, SI-based algorithms have attracted great interests from multidisciplinary researchers. Inspired by the spontaneous intelligent behaviours, a wide variety of swarm intelligence algorithms have been proposed [1], such as ant colony optimisation (ACO) imitating the ants identifying the shortest route from food source to colony; particle swarm optimisation (PSO) simulating the birds flocking by adjusting their velocity; bacterial forging optimisation (BFO) mimicking bacteria moving towards nutrient places, and artificial bee colony (ABC) emulating bee sharing food source information through dancing. Since inception, SI-based algorithms have been applied to many real-world optimization problems including power systems, portfolio optimisation, B Quande Qin qinquande@gmail.com 1 College of Mangement, Shenzhen University, Shenzhen 518060, China multiple sequence alignment, data clustering problems, neural network training, location problems and transportation management, just to name a few [2]. ...
... Since inception, SI-based algorithms have been applied to many real-world optimization problems including power systems, portfolio optimisation, B Quande Qin qinquande@gmail.com 1 College of Mangement, Shenzhen University, Shenzhen 518060, China multiple sequence alignment, data clustering problems, neural network training, location problems and transportation management, just to name a few [2]. ...
Brainstorm optimisation (BSO) algorithm is a recently developed swarm intelligence algorithm inspired by the human problem-solving process. BSO has been shown to be an efficient method for creating better ideas to deal with complex problems. The original BSO suffers from low convergence and is easily trapped in local optima due to the improper balance between global exploration and local exploitation. Motivated by the memetic framework, an adaptive BSO with two complementary strategies (AMBSO) is proposed in this study. In AMBSO, a differential-based mutation technique is designed for global exploration improvement and a sub-gradient strategy is integrated for local exploitation enhancement. To dynamically trigger the appropriate strategy, an adaptive selection mechanism based on historical effectiveness is developed. The proposed algorithm is tested on 30 benchmark functions with various properties, such as unimodal, multimodal, shifted and rotated problems, in dimensions of 10, 30 and 50 to verify their scalable performance. Six state-of-the-art optimisation algorithms are included for comparison. Experimental results indicate the effectiveness of AMBSO in terms of solution quality and convergence speed.
... Swarm intelligence-based computing is an essential branch of biological computing. To solve various problems, many computing algorithms based on swarm intelligence to simulate natural swarm models have been invented [23,24]. The more well-known and standard swarm intelligence optimization algorithms involve Particle Swarm Optimization (PSO) [25], Differential Evolution(DE) [26], Grey Wolf Optimization(GWO) [27], Ant Colony Optimization(ACO) [28], Harris Hawks Optimization(HHO) [29], Whale Optimization Algorithm(WOA) [30], Genetic Algorithm(GA) [31], Cuckoo Search Algorithm(CS) [32] and Sine and Cosine Algorithm(SCA) [33] and so on. ...
Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
... It is inspired by the intelligent, selforganizing and aggregated behavior of bee colonies. Motivated by its high efficiency in solving optimization problems [10], it was adopted to solve the community detection problem. Abu Naser and Alshattnawi [11] used ABC to detect communities in social networks by maximizing the modularity measure. ...
... This type of algorithm simulates the swarm behavior of various animals and uses the information between biological groups and between them and the environment to communicate and cooperate. Moreover, the goal of optimization is achieved through simple and limited interaction with experienced [47]. The most classic are particle swarm optimization (PSO, 1995) [48], ant colony optimization (ACO, 1996) [49], artificial fish swarm algorithm (AFSA, 2003) [50], artificial bee colony (ABC, 2006) [51], cuckoo optimization algorithm (COA, 2011) [52], grey wolf optimizer (GWO, 2014) [25]. ...
This paper introduces a new metaheuristic algorithm called bacteria phototaxis optimizer (BPO). It is designed to solving optimization issues. Inspired by the bacteria phototaxis under the control of photosensory proteins in nature, and based on the basic law of bacterial colony growth and evolution, we have designed the photosensory protein concentration, phototaxis motion and growth operators. These three operators exhibit a highly adaptive and information interaction mechanism. The goal is to simulate the phototaxis process of bacteria and form a complete model of BPO. At the same time, BPO is compared with eight most representative as well as newly generated metaheuristics. Its performance is verified by using 23 well-known benchmark functions with three different types. Additionally, we have conducted several evaluation processes, such as qualitative and quantitative analysis as well as parametric and nonparametric tests. Finally, five classical engineering design problems are used to further test the effectiveness of the algorithm in solving constrained problems. The aforementioned experimental results show that compared with other algorithms, BPO has better accuracy, convergence, and robustness and shows strong competitiveness and optimization performance.
... Nowadays, many computational algorithms or models have been presented for swarm intelligence [26] [34]. Most of these approaches are inspired by natural or social phenomena. ...
The modeling of emergent swarm intelligence constitutes a major challenge and it has been tacked in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individuals determine their behaviors based on their internal contradictions whilst they associate and in-teract to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the development of individuals’ internal contradictions and the interactions taking place between in-dividuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configu-rations of individuals’ internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applica-bility. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.
... Therefore, various controlling and optimization techniques are widely used in this area. Some famous intelligent optimizing algorithms, i.e., ACO, PSO, ABC, PIO, etc., are gaining popularity due to their problem-solving ability with the simplest structure [14][15][16][17]. Based on the food searching intelligence of real ants in nature; Ant ...
Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.
... Intelligent optimization algorithm [3][4][5] has been proved to be effective in solving parameter optimization problems [6][7][8] and has a good research and development prospect, including Sailed Fish Optimizer (SFO) [9,10], Butterfly Optimization algorithm (BOA) [11,12], Equilibrium Optimizer (EO) [13,14], and Pathfinder Algorithm (PFA) [15,16]. ...
In order to improve the accuracy and performance of traditional image threshold segmentation algorithm, this paper proposes a multithreshold segmentation method named improved Harris hawk optimization (IMHHO). Firstly, IMHHO adopts Tent map and elite opposition-based learning to initialize population and enhance the diversity. Secondly, IMHHO uses quadratic interpolation to generate new individuals and enhance the local search ability. Finally, IMHHO adopts improved Gaussian disturbance method to disturb optimal solution, which coordinates the local and global search ability. Then, the performance of IMHHO is tested based on 14 benchmark functions. In image segmentation, different algorithms are tested to compare the comprehensive performance based on Otsu and Renyi entropy. Experiments show that IMHHO performs better in the three kinds of benchmark functions; the segmentation effect is directly proportional to the number of thresholds; compared with other algorithms, IMHHO has better comprehensive performance.
... The exploitation of fish heuristics in other real-life applications, such as vehicle routing, planning and scheduling; data mining; bioinformatics; and pattern matching would very much support their utility and encourage their usage as prominent SI algo-rithms. Among the possible application areas is the new field of hardware-inspired SI [91], which aims to design hardware that evolves and interacts with its environment. To our knowledge, fish heuristics have yet to be tested in this interesting field. ...
The collective behaviour of fish schools, shoals and other swarms in nature has long inspired researchers to develop solutions for optimization problems. Instinct influences the behaviour of fish to group into schools to increase safety, enhance foraging success, and promote breeding. According to these instinctive behaviours, several fish-inspired algorithms have been introduced to solve hard problems. This paper presents a comprehensive survey of fish-inspired heuristics, exploring their evolution within the context of general optimization problems. To our knowledge, this survey is the first to cover both main fish-inspired heuristics in the literature, namely, the artificial fish swarm algorithm (AFSA) and Fish school search (FSS), in addition to other algorithms inspired by specific fish species. The review covers more than 50 papers published in the Web of Science and IEEE databases since 2000. We first review the basic fish heuristics, highlighting their advantages and drawbacks, and then detail attempts in the literature to improve their behaviour to solve complex, multi-objective and high-dimensional problems in several domains. Our work is intended to provide guidance for researchers and practitioners for the purpose of further advancing research in the area of fish-inspired heuristics. We aspire to encourage their utilization in various fields for global optimization and in real-life applications. The survey findings indicate that fish-inspired heuristics are very alive in recent literature and still have great potential. Several challenges and future research directions are also identified among the findings of this survey, which can help to enhance this vibrant line of research.
... Then their performance was compared on thirty benchmark functions. The biological metaphors of several SI-based computational algorithms were reviewed by Parpinelli and Lopes (2011), and Duan and Luo (2015). Rajasekhar et al. (2017) focused on the beeinspired algorithms and outlined their biological motivations, algorithmic features, engineering applications and prospects in the problem domains. ...
While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problems to compare the performance of these nineteen algorithms in the form they were first introduced in the literature against five state-of-the-art swarm algorithms. This comparison reveals the advancements made in this field over three decades. It reveals that, while the state-of-the-art swarm optimisation algorithms are indeed competitive in terms of the quality of solutions they find, their complexities have evolved to be more computationally demanding when compared to the nineteen original algorithms of swarm optimisation. The investigation suggests that there is an urge to continue to design swarm optimisation algorithms that are simpler, while maintaining their current competitive performance.
... PIO algorithm is one of Bio-Inspired computation algorithms, Duan and Qiao (2014), as these algorithms presented many novel techniques in several applications, Cai, Niu, Geng, et al. (2019), with different procedure, Cai, Geng, Wu, et al. (2019) .Pigeon-Inspired optimization (PIO) algorithm is an optimization algorithm based on the homing behaviours of pigeons, Duan and Luo (2015). Depending on pigeons' ability of using a combination navigation method based on sun position, magnetic field and landmarks, they use navigational tools during stages of their journey, Guilford et al. (2004). ...
... When using information in a trigger-based system, individuals act in the environment by performing specific, mostly one-off, actions, while in a follow-through, they are guided by what they find, and the action can be more long-lasting. The marker-based information is explicitly defined for interaction purposes (e.g., pheromone), while individuals implicitly share sematectonic information as the current state of the population (figure adapted from (Mamei et al. 2006)) In fact, the literature has various approaches to classify swarm systems (Parpinelli and Lopes 2011;Duan and Luo 2015;Chu et al. 2018) and metaheuristics in general (Gendreau and Potvin 2005;Fernandez-Marquez et al. 2012;Fong et al. 2015). These efforts are essential to organize the field. ...
Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction
... As a type of meta-heuristic algorithm, swarm intelligence optimization algorithm is a bionic algorithm based on population strategy. Inspired by the life processes of natural organisms, researchers have put forward a variety of swarm intelligence optimization algorithms and continue to supplement and improve upon them [40]. The main methods primarily include GA, ACO, and PSO. ...
During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.
... The use of MSI and HSI images demands a lot from the CPS concerning distributed computing [30][31][32][33]35] and fast communication links [34,35]. Since dimensionality is quite a burden, computational intelligence [36][37][38][39] is called for to handle the computational load both via software, hardware and hybrid frameworks. ...
Optical Flow (OF) depicts the pattern of apparent motion of objects within an image by analyzing at least two consecutive video frames. It can be caused by the movement of objects or cameras. It can be a 2D or a 3D vector field where each vector represents a displacement vector corresponding to the movement of points among adjacent frames.
This chapter talks about all six degrees of freedom (DOF) of an unmanned aerial vehicle (UAV) without an external reference system. OF can help to achieve a fully autonomous flight, perform 2D/3D surveillance, map regions, and avoid collisions among other things. For 2D positioning, the OF principle can be combined with the output of height estimation, fusing ultrasonic, infrared, and inertial and pressure sensor data to estimate the 3D position of the UAV for the sake of control and steering. All data processing can be done onboard, offboard, or using both strategies by exploring conveniently the cyber-physical system paradigm for indoor and outdoor applications. The focus of this chapter is 2D positioning via OF for situation awareness, detection, and avoidance systems.
... Recently, more and more nature-inspired algorithms (Yang 2014) have been proposed and generally applied in numerous applications, such as path planning , machine learning (Zhou 2016), knapsack problem (Feng et al. 2015), fault diagnosis (Duan and Luo 2015;Gao et al. 2008) and directing orbits of chaotic system (Cui et al. 2013). Among all kinds of natural-inspired algorithms, clustering algorithms and evolutionary algorithms are the most representative ones. ...
Currently, a novel of meta-heuristic algorithm called monarch butterfly optimization (MBO) is presented for solving machine learning and continuous optimization problems. It has been proved experimentally that MBO is superior to artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), Biogeography-based optimization (BBO), differential evolution algorithm (DE) and simple genetic algorithm (SGA) algorithms on most test functions. This paper presents a new version of MBO with simulated annealing (SA) strategy called SAMBO. The SA strategy is put in the migration operator and butterfly adjusting operator. So the newly proposed algorithm has two features: One is that the algorithm accepts all the butterfly individuals whose fitness are better than their parents. The other is that the algorithm randomly selects some individuals which are worse than their parents to disturbance the convergence of algorithm. In this way, the SAMBO algorithm can escape from local optima. Finally, the experiments are carried on 14 continuous nonlinear functions, and results show that SAMBO method exceeds the MBO algorithm on most test functions.
... State Matrices a n Normal acceleration at accelerometer position a 1 , a 2 , … Characteristic equation coefficients K p , K φ Roll autopilot gains K I , K q Pitch autopilot gains k 1 , k 2 , k 3 , k 4 PIO algorithm is one of Bio-Inspired computation algorithms, , as these algorithms presented many novel techniques in several applications, , with different procedure, .Pigeon-Inspired optimization (PIO) algorithm is an optimization algorithm based on the homing behaviours of pigeons, Duan et al. (2015). Depending on pigeons' ability of using a combination navigation method based on sun position, magnetic field and landmarks, they use navigational tools during stages of their journey, Guilford (2004). ...
Pigeon-inspired optimisation (PIO) algorithm is a swarm intelligence algorithm inspired by homing behaviour of pigeons. Adaptive pigeon-inspired optimisation (APIO) algorithm is introduced to provide better search efficiency and faster convergence speed than pigeon-inspired optimisation (PIO) algorithm. Through APIO the initial values for iteration starting are set near the optimum solution and solution bounds are calculated according to stability bounds, therefore the optimisation algorithm will better deal with the flying vehicles and the optimisation process can reach an optimum solution during the control ycle time of the control system rather than pigeon-inspired optimisation (PIO) algorithm. In this paper, the flight control system for a tactical missile is designed using classical proportional-integral-differential (PID) control and the controller gains will be calculated using both PIO and APIO algorithms. Finally numerical simulation using MATLAB is presented to evaluate the performance of both
algorithms.
... Inspired from these natural swarms, Swarm Intelligence (SI) constructs the computational models that describe the collaborative behaviors in decentralized and selforganized systems (Blum and Li, 2008). In recent years, SI is also applied to a wide range of fields, such as path planning, control of robotics, image processing, and communication networks (Duan and Luo, 2015). Examples of classic SI optimization methods include ant colony optimization (ACO) (Dorigo and Di Caro, 1999), particle swarm optimization (PSO) (Kennedy and Eberhart, 1999). ...
As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In this work, we explore the feasibility of connecting these two models by implementing a generalized SI model on SNN. In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing. The coupled neurons communicate and modulate each other's action potentials through event-driven spikes and synchronize their dynamics around the states of optimal solutions. We demonstrate that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions. Furthermore, we demonstrate an efficient implementation of such neural dynamics on an emerging hardware platform, namely ferroelectric field-effect transistor (FeFET) based spiking neurons. Such an emerging in-silico neuron is composed of a compact 1T-1FeFET structure with both excitatory and inhibitory inputs. We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.
... Note that, in many realworld cases, the explicit closed-form formulation of f (·) is not available. Therefore, the need to apply the metaheuristic optimization methods whose design is inspired from biological behaviors, like genetic algorithm [1], particle swarm optimization (PSO) [2,3], and pigeon inspired optimization (PIO) [4][5][6][7], is pressing. Sometimes there exist multiple objective functions that need to be minimized in a problem [8,9]. ...
Pigeon-inspired optimization (PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator, and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally, which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity—HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of “exploitation” and “exploration”, so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.
... Many traditional methods are not adequate to solve complex optimization problems, especially for those which have higher dimensions or more than one local optimum (Farnad, Jafarian, & Baleanu, 2018). Therefore, many researchers have a great interest in meta-heuristic algorithms during the recent years (Duan, 2015). Many meta-heuristic algorithms are nature-inspired (Manjarres, Landa-Torres, & Gil-Lopez, 2013), which means that they are originated from mimicking physical phenomena or the interactive behaviours of the organisms. ...
This paper presents a novel hybrid meta-heuristic algorithm called HMGSG to solve the optimization problems. In the proposed HMGSG algorithm, a spiral-shaped path for grey wolf optimization (GWO) is used to ensure both the faster convergence rate and diversity. The mutualism phase of symbiotic organisms search (SOS) is introduced and modified with the adaptive benefit factors to optimize the ability of exploitation. The stud genetic algorithm (GA) is introduced into the HMGSG to promote convergence. The numerical experiment results show that the performance of HMGSG is superior to that of the GWO, SOS and GA. In addition, the HMGSG algorithm is used to optimize the fractional-order PID controller parameters for roll attitude control of UAV. And the simulation results show the effectiveness of this algorithm.
... Algorithms state-of-art prospects and applications are defined in Agarwal and Mehta (2014). New progress in swarm intelligence-based algorithms along with current issues were presented in Duan and Luo (2015). ...
... Since genetic algorithms (GAs) [4,5] are proposed in the 1960s, various metaheuristic algorithms are put forward and used to successfully address many complicated engineering problems, such as scheduling [6], path planning [7], directing orbits of chaotic systems [8], task assignment problem [9,10], feature selection [11], wind generator optimization [12], reliability problems [13,14], knapsack problem [15], and fault diagnosis [16]. Among different kinds of metaheuristic algorithms, swarm intelligence (SI) methods [17] are one of the most representative paradigms. ...
After studying the behavior of monarch butterflies in nature, Wang et al. proposed a new promising swarm intelligence algorithm, called monarch butterfly optimization (MBO), for addressing unconstrained optimization tasks. In the basic MBO algorithm, the fixed butterfly adjusting rate is used to carry out the butterfly adjusting operator. In this paper, the self-adaptive strategy is introduced to adjust the butterfly adjusting rate. In addition, the crossover operator that is generally used in evolutionary algorithms (EAs) is used to further improve the quality of butterfly individuals. The two optimization strategies, self-adaptive and crossover operator, are combined, and then self-adaptive crossover operator is proposed. After incorporating the above strategies into the basic MBO algorithm, a new version of MBO algorithm, called Self-adaptive Monarch Butterfly Optimization (SaMBO), is put forward. Also, few studies of constrained optimization has been done for MBO research. In this paper, in order to verify the performance of our proposed SaMBO algorithm, the proposed SaMBO algorithm is further benchmarked by 21 CEC 2017 constrained optimization problems. The experimental results indicate that the proposed SaMBO algorithm outperforms the basic MBO and other five state-of-the-art metaheuristic algorithms.
... Therefore, more and more researchers have turned to modern intelligent optimization algorithms [1], which mainly include evolutionary computation [2], swarm intelligence, extreme learning machines [3], or artificial neural networks [4]. Among the different kinds of intelligent algorithms, swarm intelligence (SI) algorithms [5][6][7][8] are one of the most representative paradigms. ...
Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.
... SI generally models the collective intelligence mechanism in swarms of social insects, birds or other animals. The collective intelligence of swarms is mainly based on the information exchange between each individual in the group [23]. ...
Feature selection plays an important role in data mining and pattern recognition. However, most existing feature selection methods suffer from stagnation in local optimal and/or high computational cost. In general, feature selection process can be considered as a global optimization problem. The swarm intelligence (SI) algorithm can effectively find the optimal feature subset due to its global search ability. But it usually consumes very long running time when dealing with large datasets. In this paper, feature selection is transformed into a global optimization problem, which provides a fast and efficient method based on swarm intelligence algorithm. First, we propose a global optimization framework for filter-based feature selection and its mathematical model. Furthermore, to solve feature selection problem for acoustic defect detection, we combine the Shuffled Frog Leaping Algorithm (SFLA) with an improved minimumredundancy maximum-relevancy (ImRMR), named SFLA-ImRMR. In the experiments, a back propagation neural network (BPNN) is employed to evaluate the classification performance of the selected feature subset on the test sets of acoustic defect detection. The results show that SFLA-ImRMR achieved similar performance to the other algorithms within the shortest time.
... According to Dubey and Pandit (2014), bat algorithm differentiates itself from genetic algorithm being much easier to understand and implement due to less number of parameters. Bat algorithm can also be considered as a balanced combination of the standard PSO and the intensive stochastic local search (Duan and Luo, 2015). ...
... Other approaches are based on the application of nature-inspired metaheuristic techniques, which have been intensively applied to solve difficult optimisation problems that cannot be properly solved through traditional optimisation algorithms (Duan and Luo, 2015;Engelbretch, 2005). Genetic algorithms have been applied to this problem in both the discrete version (Sarfraz and Raza, 2001;Yoshimoto et al., 1999) and the continuous version Yoshimoto et al., 2003). ...
Surface reconstruction is an important issue in many areas: CAD/CAM (reverse engineering for automotive, aerospace and shipbuilding industries), rapid prototyping, biomedical engineering (customised prosthesis, medical implants), medical imaging (computer tomography, magnetic resonance), and others. A classical approach in the field is to consider free-form polynomial surfaces. However, the polynomial scheme cannot replicate many shapes such as the quadrics. In this paper, we overcome this limitation by using rational Bézier surfaces. This rational case is more complicated than the polynomial one, leading to a difficult over-determined nonlinear continuous optimisation problem. Our approach is based on a powerful bio-inspired technique called bat algorithm, sequentially applied in our method to compute the data parameters and weights. This process is performed iteratively with the output of each bat algorithm as the input of the next one, and so on. Then, the poles are computed by SVD least squares approximation. Our method has been applied to three illustrative examples with remarkable results. It can recover the underlying shape of complicated surfaces with good accuracy for data points affected by measurement noise and irregular sampling. Comparative work with common approaches in the field shows that our method outperforms them for all instances in this paper.
... A swarm intelligence optimization algorithm is based on the interaction and cooperation between individuals in a group of organisms [8,9]. The behavior and intelligence of each individual is simple and limited, but the swarm will produce inestimable overall capacity by interaction and cooperation [10]. ...
Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show that proposed algorithm has high accuracy and stability compared with the classical particle swarm optimization and artificial bee colony algorithm.
... Other approaches are based on the application of nature-inspired metaheuristic techniques, which have been intensively applied to solve difficult optimisation problems that cannot be properly solved through traditional optimisation algorithms (Duan and Luo, 2015;Engelbretch, 2005). Genetic algorithms have been applied to this problem in both the discrete version (Sarfraz and Raza, 2001;Yoshimoto et al., 1999) and the continuous version Yoshimoto et al., 2003). ...
... FA is a population-based swarm intelligent search algorithm [11]. Each individual firefly in population has a role as a candidate solution in the search space. ...
In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.
... As a branch research field of artificial intelligence, it became increasingly popular over the last decade [1]. Recently, algorithms of swarm intelligence are applied not only in optimization, but also in various field like image processing, path planning, control of robotics, and telecommunication networks [2]. Examples of notable swarm-intelligence optimization methods include ant colony optimization (ACO) [3], particle swarm optimization (PSO) [4]. ...
... According to Dubey and Pandit (2014), bat algorithm differentiates itself from genetic algorithm being much easier to understand and implement due to less number of parameters. Bat algorithm can also be considered as a balanced combination of the standard PSO and the intensive stochastic local search (Duan and Luo, 2015). ...
... The pigeon swarm optimization algorithm (PSOA) is proposed by Duan and Quiao [30,31] in 2014. The algorithm is derived from the behavior of homing pigeons. ...
At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage.
... The basic ACO algorithm has been used previously by Garcia et al. (2009) andZhangqi et al. (2011), but there is lot of scopes to improve the time consuming probabilistic selection formula and pheromone updation scheme. ACO algorithm is a swarm intelligence technique (Duan and Luo, 2015;Wang et al., 2015) that simulates the behavioural working of real ants. The ants use pheromones to find their path from source to destination (Dorigo and Stützle, 2004). ...
Automated navigation is a pivotal task of robotics research and the key challenge lies in robot motion on unknown dynamic terrain. The large number of solutions to robotic path planning, especially in unknown and dynamic environments, mainly rely on the heuristic methods. The most important factor for this choice is the fast convergence towards solution without supervision. In the proposed scheme we have used a modified version of ant colony optimisation. We incorporated the directional movement history of robot on a grid into a vector as a probability multiplication factor which helps to achieve a faster convergence and avoid unnecessary movements, e.g., looping. In this work we have devised a novel pheromone updation scheme. Along with this we have applied path smoothing to lessen the number of turns on the candidate optimal path. Effectiveness is shown through several extensive experiments and results clearly indicate the aptness of the proposed scheme.
... However, premature convergence is the shortcoming of GA, and the speed of convergence is slow in later iteration process. Swarm intelligence based computation is an important approach of address optimization problem, and it mainly focuses on the collective behavior of decentralized, selforganized systems [41]. Ant colony optimization algorithms [42][43][44] are applied to detect community in complex networks. ...
Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC) algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.
... Over the past few years, various kinds of metaheuristic algorithms have been proposed and successfully applied to solve myriads of real world optimisation problems. Among all metaheuristic methods, swarm-based algorithms (Cui and Gao, 2012;Duan and Luo, 2015;Gopinadh and Singh, 2015;Jr et al., 2015) are one of the most representative paradigms and widely used ones. ...
In this paper, a new swarm-based metaheuristic algorithm, called elephant herding optimisation EHO, is proposed for solving global optimisation tasks, which is inspired by the herding behaviour of the elephant groups. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when growing up. These two behaviours can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants are updated using its current position and matriarch through clan updating operator, and the separating operator is then implemented. Moreover, EHO has been benchmarked by 20 standard benchmarks, and two engineering cases in comparison with BBO, DE and GA. The results clearly establish the supremacy of EHO in finding the better function values on most test problems than those three algorithms. The code can be found in the website: http://www.mathworks.com/matlabcentral/fileexchange/53486.
... Over the past few years, various kinds of metaheuristic algorithms have been proposed and successfully applied to solve myriads of real world optimisation problems. Among all metaheuristic methods, swarm-based algorithms (Cui and Gao, 2012;Duan and Luo, 2015;Gopinadh and Singh, 2015;Jr et al., 2015) are one of the most representative paradigms and widely used ones. ...
... Starting with initial feasible solutions, these algorithms try to explore the feasible region and also explore the neighbourhood of the solutions by moving in a guided random movement. Two major classes of these solution methods are evolutionary computation and swarm intelligence (Fister et al., 2015;Duan and Luo, 2015). Most of these algorithms are inspired by a given natural scenario (Akerkar and Sajja, 2009). ...
The firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. The FA is used to solve various optimization problems, but it still has some deficiencies, such as high complexity, slow convergence rate, and low accuracy of the solution. This paper proposes a highly efficient quantum firefly algorithm with stochastic search strategies (QSSFA). In QSSFA, individuals are generated in the way of quantum angle coding by introducing the laws of quantum physics and quantum gates, and combined with the random neighborhood attraction model, an adaptive step size strategy is also introduced in the optimization. The complexity of the algorithm is greatly reduced, and the global search ability of the algorithm is optimized. The convergence speed of the algorithm, the ability to jump out of the local optimum, and the algorithm accuracy are improved. The proposed QSSFA’s performance is tested on ten mathematical test functions. The obtained results show that the QSSFA algorithm is very competitive compared to the firefly algorithm and three other FA variants.
In this article, the formation control of quad-rotor unmanned aerial vehicle (UAV) via pigeon inspired optimization (PIO) is designed. The nonlinear mathematical model of the quad-rotor UAV is used by applying algebraic graph theory and matrix analysis. A high order consistent formation control algorithm with fixed control topology is designed by using a position deviation matrix to describe its formation To control the attitude of quad-rotor UAVs, it is difficult to obtain a set of optimal solutions, and hence a PIO based algorithm with variable weight hybridization is proposed. The algorithm is mainly composed of two parts. First, according to the distance between the particles in the iterative process, the inertia weight is dynamically changed, and the coefficient is adjusted to control the degree of influence on its inertia weight. Second, the overall scenario is designed by using MATLAB based simulations which show that the formation control of the quad-rotor UAV is achieved with the help of PIO.
This study proposes a novel hybrid strategy for formation control of a swarm of multiple unmanned aerial vehicles (UAVs). To enhance the fitness function of the formation, this research offers a three-dimensional formation control for a swarm using particle swarm optimization (PSO) with Cauchy mutant (CM) operators. We use CM operators to enhance the PSO algorithm by examining the varying fitness levels of the local and global optimal solutions for UAV formation control. We establish the terrain and the fixed-wing UAV model. Furthermore, it also models different control parameters of the UAV as well. The enhanced hybrid algorithm not only quickens the convergence rate but also improves the solution optimality. Lastly, we carry out the simulations for the multi-UAV swarm under terrain and radar threats and the simulation results prove that the hybrid method is effective and gives better fitness function.
Particle swarm optimization (PSO) is one of the most popular stochastic swarm-based metaheuristic algorithms. Kalman filter principle is introduced to predict the global optimum more accurately to enhance convergence. However, the evolution of particles in current Kalman PSO merely depends on the adjustment based on observation. In this paper, a modified Kalman particle swarm optimization (MKPSO) algorithm is proposed. The population is extended with the estimated optimum based on Kalman filtering, in which the prediction model is formulated as the weighted central optimum. Benchmark functions in the CEC14 test suite are adopted to verify the effectiveness of MKPSO. Numerical results show that MKPSO is more effective in mining capability for high-dimensional problems. Besides, the superiority of MKPSO lies in solving hybrid optimization problems. At last, MKPSO is applied to maximize the attainable moments subset of very flexible aircraft (VFA) on account for redundancy of control surfaces. Simulation results reveal that there is a trade-off between flight and control performance for VFA.
Monarch butterfly optimization (MBO) is a newly proposed meta-heuristic algorithm for solving optimization problems. It has been proved experimentally that MBO is superior to the other five state-of-the-art meta-heuristic algorithms on most test problems. This paper presents a new improved MBO algorithm with Simulated Annealing (SA) strategy, in which the SA strategy is involved into the migration operator and butterfly adjusting operator. So the newly proposed algorithm SAMBO not only accepts all the butterfly individuals whose fitness are better than their parents’ but also randomly selects some individuals that are worse than their parents to disturbance the convergence of algorithm. In the final, the experiments are carried out on 14 famous continuous nonlinear functions; the results demonstrate that SAMBO algorithm is significantly better than the original MBO algorithm on most test functions.
Self-organization is a natural phenomenon that emerges in systems with a large number of interacting components. Self-organized systems show robustness, scalability, and flexibility, which are essential properties when handling real-world problems. Swarm intelligence seeks to design nature-inspired algorithms with a high degree of self-organization. Yet, we do not know why swarm-based algorithms work well and neither we can compare the different approaches in the literature. The lack of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without much regard as to whether they are similar to already existing approaches. We address this gap by introducing a network-based framework — the interaction network — to examine computational swarm-based systems via the optics of social dynamics. We discuss the social dimension of several swarm classes and provide a case study of the Particle Swarm Optimization. The interaction network enables a better understanding of the plethora of approaches currently available by looking at them from a general perspective focusing on the structure of the social interactions.
[arxiv] https://arxiv.org/abs/1811.03539
Metaheuristics have been extensively used for solving difficult optimization problems in recent years. However, according to the famous theory (No Free Lunch), it is infeasible for a metaheuristic to optimally solve all problems. As a result, novel metaheuristics have been incessantly introduced. In this paper, we provide a bibliography on recent development in metaheuristics. This work lists and categorizes 112 metaheuristic articles published from 2009 to 2015. The key aim of this work is to gather a group of metaheuristics that plays a fundamental role in development of metaheuristics in the coming years. This paper reveals that the growth of new metaheuristics continues as well as has not seen any interruption. Although this study cannot assert to be comprehensive, it involves a main part of novel publications and therefore, is a helpful guide for new metaheuristic researches.
The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of “problem centered + evolutionary solution” and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony’s labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs’ real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.
The future of designing optical networks is focused on the wavelength division multiplexing (WDM) technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of fiber. However, when it is necessary to establish a set of demands, a problem comes up. This problem is known as a routing and wavelength assignment (RWA) problem. Depending on the traffic pattern, two varieties of a RWA problem have been considered in the literature: static and dynamic. In this paper, we present a comparative study among three multiobjective evolutionary algorithms (MOEAs) based on swarm intelligence to solve the RWA problem in real-world optical networks. Artificial bee colony (ABC) algorithm, gravitational search algorithm (GSA), and firefly algorithm (FA) are the selected evolutionary algorithms, but are adapted to multiobjective domain (MO-ABC, MO-GSA, and MO-FA, respectively). In order to prove the goodness of the swarm proposals, we have compared them with a standard MOEA: fast nondominated sorting genetic algorithm. Finally, we present a comparison among the metaheuristics based on swarm intelligence and several techniques published in the literature, coming to the conclusion that swarm intelligence is very suitable to solve the RWA problem, and presumably that it may obtain such quality results not only in diverse telecommunication optimization problems, but also in other engineering optimization problems.
Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly's movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.
Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.
It is now five years since the launch of the International Journal of
Bio-Inspired Computation (IJBIC). At the same time, significant new progress
has been made in the area of bio-inspired computation. This review paper
summarizes the success and achievements of IJBIC in the past five years, and
also highlights the challenges and key issues for further research.
Annual crop planning (ACP) is an NP-hard type optimization problem in agricultural planning. It involves finding the optimal solution for the seasonal hectare allocations of a limited amount of agricultural land, among various competing crops that are required to be grown on it. This study investigates the effectiveness of employing three relatively new swarm intelligence (SI) metaheuristic techniques in determining the solutions to the ACP problem with case study from an existing irrigation scheme. The SI metaheuristics studied are cuckoo search (CS), firefly algorithm (FA), and glowworm swarm optimization (GSO). Solutions obtained from these techniques are compared with that of a similar population-based technique, namely, genetic algorithm (GA). Results obtained show that each of the three SI algorithms provides superior solutions for the case studied.
Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature.
We consider a new QoS framework for a wide range of multimedia services in ever-changing networks where traffic is dynamic and network topologies frequently change. In this paper, for such networks, we propose a new self-organizing QoS framework called AntQoS, which is inspired by recent works on ant colony optimization. Compared with previous QoS frameworks, AntQoS provides two quite promising capabilities: 1) the self-organized network QoS (not pre-defined QoS), which autonomously reconfigures itself by promptly adapting to changing network environments such as sudden arrivals of highly bursty traffic, and 2) the self-organized network controls, which autonomously resolve the network congestion and intrinsically resist the multiple network failures. These two promising capabilities enable the proposed AntQoS framework to efficiently and reliably support a wide range of multimedia services with various quality demands. Especially, these capabilities are provided by making use of swarm intelligence of artificial ants without any supervised control. For this purpose, AntQoS employs one single artificial ant colony on an ever-changing network; a number of artificial ants in the employed colony explore network and measure or gather the status information about the networks. Based on the gathered information, AntQoS organizes and maintains a small number of virtual sub colonies named QoS colonies. The QoS colony is an intelligent virtual colony to be capable of searching the path which guarantees the given quality demands of flows. In addition, it is autonomously generated, maintained, and deleted for promptly adapting to the ever-changing network status. Simulation results demonstrate that AntQoS successfully supports various multimedia services with diverse delay requirements while increasing the network throughput by approximately 20% compared to the well-known IntServ frameworks. Simulation results also show that AntQoS autonomously redistributes the congested traf- ic and resists the unexpected network failures.
Inspired by swarm intelligence observed in social species, the artificial self-organized networking (SON) systems are expected to exhibit some intelligent features (e.g., flexibility, robustness, decentralized control, and self-evolution, etc.) that may have made social species so successful in the biosphere. Self-organized networks with swarm intelligence as one possible solution have attracted a lot of attention from both academia and industry. In this paper, we survey different aspects of bio-inspired mechanisms and examine various algorithms that have been applied to artificial SON systems. The existing well-known bio-inspired algorithms such as pulse-coupled oscillators (PCO)-based synchronization, ant- and/or bee-inspired cooperation and division of labor, immune systems inspired network security and Ant Colony Optimization (ACO)-based multipath routing have been surveyed and compared. The main contributions of this survey include 1) providing principles and optimization approaches of variant bio-inspired algorithms, 2) surveying and comparing critical SON issues from the perspective of physical-layer, Media Access Control (MAC)-layer and network-layer operations, and 3) discussing advantages, drawbacks, and further design challenges of variant algorithms, and then identifying their new directions and applications. In consideration of the development trends of communications networks (e.g., large-scale, heterogeneity, spectrum scarcity, etc.), some open research issues, including SON designing tradeoffs, Self-X capabilities in the 3^rd Generation Partnership Project (3GPP) Long Term Evolution (LTE)/LTE-Advanced systems, cognitive machine-to-machine (M2M) self-optimization, cross-layer design, resource scheduling, and power control, etc., are also discussed in this survey.
In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture, programming strategy, and probabilistic spike-timing dependent plasticity (STDP) learning rule for two different CBRAM configurations with-selector (1T-1R) and without-selector (1R) are proposed. We show two methods (intrinsic and extrinsic) for implementing probabilistic STDP rules. Fully unsupervised learning with binary synapses is illustrated through two example applications: 1) real-time auditory pattern extraction (inspired from a 64-channel silicon cochlea emulator); and 2) visual pattern extraction (inspired from the processing inside visual cortex). High accuracy (audio pattern sensitivity > 2, video detection rate > 95%) and low synaptic-power dissipation (audio 0.55 μW, video 74.2 μW) are shown. The robustness and impact of synaptic parameter variability on system performance are also analyzed.
The fundamental limit for small antennas provides a guide to the
effectiveness of designs. Gustafsson et al, Yaghjian et al, and
Mohammadpour-Aghdam et al independently deduced a variation of the
Chu-Harrington limit for planar antennas in different forms. Using a
multi-parameter optimisation technique based on the ant colony algorithm,
planar, meander dipole antenna designs were selected on the basis of lowest
resonant frequency and maximum radiation efficiency. The optimal antenna
designs across the spectrum from 570 to 1750 MHz occupying an area of 56mm
* 25mm were compared with these limits calculated using the
polarizability tensor. The results were compared with Sievenpiper's comparison
of published planar antenna properties. The optimised antennas have greater
than 90% polarizability compared to the containing conductive box in the range
0.3<ka<1.1, so verifying the optimisation algorithm. The generalized
absorption efficiency of the small meander line antennas is less than 50%, and
results are the same for both PEC and copper designs.
Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats' behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.
A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.
In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish different taxonomies to categorize tuning methods and review existing work. Finally, we elaborate on how tuning can improve methodology by facilitating well-funded experimental comparisons and algorithm analysis.
This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.
This paper proposes a distributed resource assignment strategy for cognitive networks mimicking a swarm foraging mechanism, assuming that the communication among the cognitive nodes is impaired by random link failures and quantization noise. Using results from stochastic approximation theory, we propose a swarm mechanism that converges almost surely to a final allocation even in the presence of imperfect communication scenarios. The theoretical findings are corroborated by numerical results showing that the only effect of the random link failures is to decrease the convergence rate of the algorithm. We propose then a fast swarming approach, robust to random disturbances, that adapts its behavior with respect to the interference power perceived by every node, thus increasing the speed of convergence and improving the resource allocation capabilities.
It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto dominance relation for a high-dimensional space leads diversity maintenance mechanisms to play the leading role during the evolutionary process, while the preference of diversity maintenance mechanisms for individuals in sparse regions results in the final solutions distributed widely over the objective space but distant from the desired Pareto front. Intuitively, there are two ways to address this problem: 1) modifying the Pareto dominance relation and 2) modifying the diversity maintenance mechanism in the algorithm. In this paper, we focus on the latter and propose a shift-based density estimation (SDE) strategy. The aim of our study is to develop a general modification of density estimation in order to make Pareto-based algorithms suitable for many-objective optimization. In contrast to traditional density estimation which only involves the distribution of individuals in the population, SDE covers both the distribution and convergence information of individuals. The application of SDE in three popular Pareto-based algorithms demonstrates its usefulness in handling many-objective problems. Moreover, an extensive comparison with five state-of-the-art EMO algorithms reveals its competitiveness in balancing convergence and diversity of solutions. These findings not only show that SDE is a good alternative to tackle many-objective problems, but also present a general extension of Pareto-based algorithms in many-objective optimization.
In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.
Optimization algorithms are normally influenced by meta-heuristic approach.
In recent years several hybrid methods for optimization are developed to find
out a better solution. The proposed work using meta-heuristic Nature Inspired
algorithm is applied with back-propagation method to train a feed-forward
neural network. Firefly algorithm is a nature inspired meta-heuristic
algorithm, and it is incorporated into back-propagation algorithm to achieve
fast and improved convergence rate in training feed-forward neural network. The
proposed technique is tested over some standard data set. It is found that
proposed method produces an improved convergence within very few iteration.
This performance is also analyzed and compared to genetic algorithm based
back-propagation. It is observed that proposed method consumes less time to
converge and providing improved convergence rate with minimum feed-forward
neural network design.
Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of
some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for
the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search
experience and possibly available heuristic information. Since the proposal of the Ant System, the first ACO algorithm, many
significant research results have been obtained. These contributions focused on the development of high-performing algorithmic
variants, the development of a generic algorithmic framework for ACO algorithms, successful applications of ACO algorithms
to a wide range of computationally hard problems, and the theoretical understanding of properties of ACO algorithms. This
chapter reviews these developments and gives an overview of recent research trends in ACO.
The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.
Various natural systems teach us that very simple individual organisms can create systems able to perform highly complex tasks by dynamically interacting with each other. The Bee Colony Optimization Metaheuristic (BCO) is proposed in this paper. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. The BCO is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty. The development of the new heuristic algorithm for the Ride-matching problem using the proposed approach serves as an illustrative example and shows the characteristics of the proposed concepts.
Quaternions are a number system, which extends complex numbers. They are especially useful in areas where fast rotation calculations are needed, e.g., programming video games or controllers of spacecraft. This paper proposes to use quaternion for the representation of individuals in firefly algorithm so as to enhance the performance of the firefly algorithm and to avoid any stagnation. The preliminary results of our experiments after optimizing a test-suite consisting of ten standard functions, showed that the proposed firefly algorithms using quaternion's representation improved the results of the original firefly algorithm.
Pigeon-Inspired Optimization (PIO) algorithm is a recently proposed bio-inspired swarm intelligence optimizer. High convergence speed is its most outstanding advantage. However, PIO algorithm can easily trap into a local optimal solution, which is the main defect that limits its further application. To overcome this defect, a Prey-Predator PIO algorithm is proposed in this paper, which combines the standard Pigeon-Inspired Optimization algorithm and the Prey-Predator strategy. This new algorithm can avoid the disadvantage which standard Pigeon-Inspires optimization has. In this paper, comparative experiments on the Proportion-Integral-Derivative (PID) parameter adjustment are conducted by using Particle-Swarm Optimization (PSO), PIO and Prey-Predator PIO, and the comparative results demonstrate our proposed approach is more feasible and effective.
Purpose
– The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems.
Design/methodology/approach
– The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted.
Findings
– The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases.
Originality/value
– In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.
Bloom filter (BF) is a simple but powerful data structure that can check membership to a static set. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the hash bitmap is sufficiently large. Spam is an irrelevant or inappropriate message sent on the internet to a large number of newsgroups or users. A spam word is a list of well-known words that often appear in spam mails. The proposed system of bin Bloom filter (BBF) groups the words into number of bins with different false positive rates based on the weights of the spam words. Cuckoo search (CS) and bat algorithm are bio-inspired algorithms that imitate the way cuckoo breeding and microbat foraging behaviours respectively. This paper demonstrates the CS and bat algorithm for minimising the total membership invalidation cost of the BBFs by finding the optimal false positive rates and number of elements stored in every bin. The experimental results demonstrate the application of CS and bat algorithm for various numbers of bins and strings.
A new ramped convergence particle swarm optimization (RCPSO) algorithm to design radio frequency identification (RFID) reader antenna for gain optimization is presented in this paper. The algorithm breaks down the optimization problem by considering only a subset of dimensions at a time, hence overcoming curse of dimensionality. It uses a multi-start approach that alleviates premature convergence. It applies heterogeneous boundary conditions to errant particles. In this paper, the effectiveness of using RCPSO for RFID reader antenna design will be investigated by a number of iterations required to achieve optimum solution along with the quality of solution. RCPSO is built specifically for antenna design. For the microstrip antenna, a significant bandwidth improvement is achieved and the overall antenna dimensions are kept practically the same. The circular gain improves by a significant 2 dBiC over super high frequency (SHF).
Swarm intelligence SI and artificial immune system AIS are both derived from imitation of nature biology system. Their common characteristic is that they both have simple individuals but appear emergence characteristic in population level through interaction among individuals. In order to explore inherent similarity and difference of complex system, we take two typical forms of swarm intelligence ant colony system and particle swarm optimisation, and AIS as objectives to study this characteristic in this paper. First, we discuss the similarity between two biology systems from system structure and operation mechanism. In addition, we also illustrate the difference between two systems from algorithm design, individual diversity and shape space. At the end of the paper, numerical experiment is used to test the performance of swarm intelligence techniques and AIS, where benchmark test functions include unimodal and multimodal function optimisation problems. Besides, combined with a concrete example, travelling salesman problem TSP, the generality and feature of these two systems in solving complex problems are discussed in detail. The objective of the paper is to set up inherent connection and difference between two unlike systems, which not only has important theoretical significance but also has significant actual value to reveal production and operation mechanism of human intelligence.
The design of electromagnetic devices is generally multimodal, multidimensional, and constrained. Its simulation-based feature makes traditional optimization algorithms unsuitable to use; whereas metaheuristic approaches are good choices for tackling such problems. Since many electromagnetic design problems require excessive computer resources even on modern computers, attempts to improve the efficiency and effectiveness of metaheuristic approaches are very challenging for designers. This paper proposes a modification of artificial bee colony (ABC) algorithm. The design philosophy of this algorithm is to promote the convergence rate of ABC. The properties of the modified ABC algorithm are analyzed by testing, first, on two mathematical functions. The proposed algorithm is then applied to solve a loudspeaker design problem using FEM. Promising performance is obtained when compared with those using original ABC, genetic algorithm, particle swarm optimization, and differential evolution algorithms.
This paper presents a binary Firefly Algorithm (FA), for cryptanalysis of knapsack cipher algorithm so as to deduce the meaning of an encrypted message (i.e. to determine a plaintext from the cipher text). The implemented algorithm has been characterized, in this paper, by a number of properties and operations that build up and evolve the fireflies' positions. These include light intensity, distances, attractiveness, and position updating, fitness evaluation. The results of the Firefly algorithm are compared with the results shown by Genetic Algorithm (GA), to discover the plaintext from the cipher text. Experimental results show that binary firefly algorithm is capable of finding correct results more efficiently than GA.
This paper proposes a hybrid firefly algorithm (HFA) to assist in decision-making for reactor arrangement in underground cable transmission systems. The HFA method is proposed based on the analysis of phototaxis behavior of fireflies, and enables solving the optimization problem effectively. In this study, by formulating high relationships among connected reactors, sheath loss, and induced voltage, the HFA method is employed to determine the appropriate reactor placement in an underground transmission systems. Through the tests made on different transmission lines along with the results compared to other methods, the proposed approach provides satisfactory decision support for reactor placement and serves as a beneficial reference for underground transmission planning and design.
Many applications of wireless sensor network (WSN) require the execution of several computationally intense in-network processing tasks. Collaborative in-network processing among multiple nodes is essential when executing such a task due to the strictly constrained energy and resources in single node. Task allocation is essential to allocate the workload of each task to proper nodes in an efficient manner. In this paper, a modified version of binary particle swarm optimization (MBPSO), which adopts a different transfer function and a new position updating procedure with mutation, is proposed for the task allocation problem to obtain the best solution. Each particle in MBPSO is encoded to represent a complete potential solution for task allocation. The task workload and connectivity are ensured by taking them as constraints for the problem. Multiple metrics, including task execution time, energy consumption, and network lifetime, are considered a whole by designing a hybrid fitness function to achieve the best overall performance. Simulation results show the feasibility of the proposed MBPSO-based approach for task allocation problem in WSN. The proposed MBPSO-based approach also outperforms the approaches based on genetic algorithm and BPSO in the comparative analysis.
This paper proposes a new multiobjective self-adaptive learning bat-inspired algorithm to solve practical reserve constrained dynamic environmental/economic dispatch that considers realistic constraints such as valve-point effects, transmission losses, and ramp rate limits over a short-term time period. Furthermore, to ensure secure real-time power system operations, the system operator must schedule sufficient resources to meet energy demand and operating reserve requirements simultaneously. The proposed problem is a complex nonlinear nonsmooth and nonconvex multiobjective optimization problem whose complexity is increased when considering the above constraints. To this end, this paper utilizes a newly developed meta-heuristic bat inspired algorithm to achieve the set of nondominated (Pareto-optimal) solutions. This algorithm is equipped with a novel self-adaptive learning to increase the population diversity and amend the convergence criteria. The initial population of the proposed framework is generated by a chaos-based strategy. In addition, a tournament crowded selection approach is implemented to choose the population such that the Pareto-optimal front is distributed uniformly, while the extreme points of the tradeoff surface are achieved simultaneously. Numerical results evaluate the performances of the framework for real-size test systems.
Particle swarm optimization (PSO) has attracted much attention and has been applied to many scientific and engineering applications in the last decade. Most recently, an intelligent augmented particle swarm optimization with multiple adaptive methods (PSO-MAM) was proposed and was demonstrated to be effective for diverse functions. However, inherited from PSO, the performance of PSO-MAM heavily depends on the settings of three parameters: the two learning factors and the inertia weight. In this paper, we propose a parameter control mechanism to adaptively change the parameters and thus improve the robustness of PSO-MAM. A new method, adaptive PSO-MAM (APSO-MAM) is developed that is expected to be more robust than PSO-MAM. We comprehensively evaluate the performance of APSO-MAM by comparing it with PSO-MAM and several state-of-the-art PSO algorithms and evolutionary algorithms. The proposed parameter control method is also compared with several existing parameter control methods. The experimental results demonstrate that APSO-MAM outperforms the compared PSO algorithms and evolutionary algorithms, and is more robust than PSO-MAM.
The initial state of an Unmanned Aerial Vehicle (UAV) system and the relative state of the system, the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization (CPTD) method. The approximation piecewise linearization control inputs are used to substitute for the continuous inputs. In this way, the multi-UAV formation reconfiguration problem can be formulated as an optimal control problem with dynamical and algebraic constraints. With strict constraints and mutual interference, the multi-UAV formation reconfiguration in 3-D space is a complicated problem. The recent boom of bio-inspired algorithms has attracted many researchers to the field of applying such intelligent approaches to complicated optimization problems in multi-UAVs. In this paper, a Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. This new approach combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which can find the time-optimal solutions simultaneously. The proposed HPSOGA will also be compared with basic PSO algorithm and the series of experimental results will show that our HPSOGA outperforms PSO in solving multi-UAV formation reconfiguration problem under complicated environments.
From a computational point of view, the identification of Bouc-Wen (BW) hysteresis model is a hard task due to the large number of parameters to be found. This is one of the reasons for which it is rarely used for modeling magnetic hysteresis, where other hysteresis models are widely used (e.g., Preisach and Jiles–Atherton). However, the opportunities that the differential expression of BW model could offer for its use in more complex computation task (e.g., nonlinear inductors inserted into a circuit and so on) justify a deeper investigation on its adoption in ferromagnetism. In this paper, using a new hybrid heuristic called metric-topological–evolutionary optimization (MeTEO), the BW identification is presented. MeTEO is a powerful algorithm based on a synergic and strategic use of three evolutionary heuristics: 1) the flock-of-starlings optimization, which shows not only high exploration capability, but also a lack of convergence; 2) the particle swarm optimization, which has a good convergence capability; and 3) the bacterial chemotaxis algorithm, which has no collective behavior or exploration skill, but has high convergence capability. MeTEO is designed to use parallel architectures and exploits the fitness modification technique. Numerical validations are presented in comparison with the performances obtained using other approaches available in the literature.
Load pattern clustering based on the shape of the electricity consumption is a key tool to provide enhanced knowledge on the nature of the consumption and assist meaningful customer partitioning. This paper presents new developments to group the load patterns using an initial set of centroids specified according to a user-defined centroid model. The original Electrical Pattern Ant Colony Clustering (EPACC) algorithm is illustrated, highlighting its characteristics and parameters, with centroids evolution during the iterative process until stabilization. The EPACC results are compared with those obtained from the classical k-means algorithm to group the representative load patterns taken from a set of non-residential customers in typical weekdays.
Particle swarm optimization (PSO), a relatively new population-based intelligence algorithm, exhibits good performance on optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged most likely around the local solution. A new optimization algorithm called multifrequency vibrational PSO is significantly improved and tested for two different test cases: optimization of six different benchmark test functions and direct shape optimization of an airfoil in transonic flow. The algorithm emphasizes a new mutation application strategy and diversity variety, such as global random diversity and local controlled diversity. The results offer insight into how the mutation operator affects the nature of the diversity and objective function value. The local controlled diversity is based on an artificial neural network. As far as both the demonstration cases' problems are considered, remarkable reductions in the computational times have been accomplished.
The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.
Bio-inspired hardware (BHW) refers to hardware that can change its architecture and behaviour dynamically and autonomously by interacting with its environment, and ant colony optimization is a meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behaviour of real ant colonies. In this paper, we take a broad survey on the recent progresses of ant colony optimization-based BHW, which includes ant colony optimization-based fuzzy controller, ant colony optimization-based hardware for the Travelling Salesman Problem (TSP), digital circuits, digital infinite impulse-response (IIR) filters, hardware-oriented ant colony optimization with look-up table and hardware/software partition. Some important issues of the challenges of ant colony optimization-based BHW are also presented. Online realization, robustness, generalization, disaster problems, theoretical analysis, implementation, swarm robotics, applications and hybrid approaches are eight key challenging issues for the ant colony optimization-based BHW.
The optimal selection of parameters for time-delay embedding is crucial to the analysis and the forecasting of chaotic time series. Although various parameter selection techniques have been developed for conventional uniform embedding methods, the study of parameter selection for nonuniform embedding is progressed at a slow pace. In nonuniform embedding, which enables different dimensions to have different time delays, the selection of time delays for different dimensions presents a difficult optimization problem with combinatorial explosion. To solve this problem efficiently, this paper proposes an ant colony optimization (ACO) approach. Taking advantage of the characteristic of incremental solution construction of the ACO, the proposed ACO for nonuniform embedding (ACO-NE) divides the solution construction procedure into two phases, i.e., selection of embedding dimension and selection of time delays. In this way, both the embedding dimension and the time delays can be optimized, along with the search process of the algorithm. To accelerate search speed, we extract useful information from the original time series to define heuristics to guide the search direction of ants. Three geometry- or model-based criteria are used to test the performance of the algorithm. The optimal embeddings found by the algorithm are also applied in time-series forecasting. Experimental results show that the ACO-NE is able to yield good embedding solutions from both the viewpoints of optimization performance and prediction accuracy.
This paper proposes the design of fuzzy controllers by ant colony optimization (ACO) incorporated with fuzzy-Q learning, called ACO-FQ, with reinforcements. For a fuzzy inference system, we partition the antecedent part a priori and then list all candidate consequent actions of the rules. In ACO-FQ, the tour of an ant is regarded as a combination of consequent actions selected from every rule. Searching for the best one among all combinations is partially based on pheromone trail. We assign to each candidate in the consequent part of the rule a corresponding Q -value. Update of the Q -value is based on fuzzy-Q learning. The best combination of consequent values of a fuzzy inference system is searched according to pheromone levels and Q -values. ACO-FQ is applied to three reinforcement fuzzy control problems: (1) water bath temperature control; (2) magnetic levitation control; and (3) truck backup control. Comparisons with other reinforcement fuzzy system design methods verify the performance of ACO-FQ.
This study presents a non-linear dual-mode receding horizon control (RHC) approach to investigate the formation flight problem for multiple unmanned air vehicles (UAVs) under complicated environments. A chaotic particle swarm optimisation (PSO)-based non-linear dual-mode RHC method is proposed for solving the constrained non-linear systems. The presented chaotic PSO derives both formation model and its parameter values, and the control sequence is predicted in this way, which can also guarantee the global convergence speed. A dual-model control strategy is used to improve the stability and feasibility for multiple UAVs formation flight controller, and the state-feedback control is also adopted, where the model is based on the invariant set theory. Series experimental results show the feasibility and validity of the proposed control algorithm over other algorithms. The proposed approach is also a promising control strategy in solving other complicated real-world problems.
The applications of recently developed meta-heuristics in cluster analysis, such as particle swarm optimization (PSO) and differential evolution (DE), have increasingly attracted attention and popularity in a wide variety of communities owing to their effectiveness in solving complicated combinatorial optimization problems. Here, we propose to use a hybrid of PSO and DE, known as differential evolution particle swarm optimization (DEPSO), in order to further improve search capability and achieve higher flexibility in exploring the natural while hidden data structures of data of interest. Empirical results show that the DEPSO-based clustering algorithm achieves better performance in terms of the number of epochs required to reach a pre-specified cutoff value of the fitness function than either of the other approaches used. Further experimental studies on both synthetic and real data sets demonstrate the effectiveness of the proposed method in finding meaningful clustering solutions.
Path planning of Uninhabited Combat Air Vehicle (UCAV) is a rather complicated global optimum problem which is about seeking a superior flight route considering the different kinds of constrains under complex combat field environment. Artificial Bee Colony (ABC) algorithm is a new optimization method motivated by the intelligent behavior of honey bees. In this paper, we propose an improved ABC optimization algorithm based on chaos theory for solving the UCAV path planning in various combat field environments, and the implementation procedure of our proposed chaotic ABC approach is also described in detail. Series of experimental comparison results are presented to show the feasibility, effectiveness and robustness of our proposed method.
In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations—uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations.
Swarm intelligence has emerged as a worthwhile class of clustering methods due to its convenient implementation, parallel capability, ability to avoid local minima, and other advantages. In such applications, clustering validity indices usually operate as fitness functions to evaluate the qualities of the obtained clusters. However, as the validity indices are usually data dependent and are designed to address certain types of data, the selection of different indices as the fitness functions may critically affect cluster quality. Here, we compare the performances of eight well-known and widely used clustering validity indices, namely, the Caliński-Harabasz index, the CS index, the Davies-Bouldin index, the Dunn index with two of its generalized versions, the I index, and the silhouette statistic index, on both synthetic and real data sets in the framework of differential-evolution-particle-swarm-optimization (DEPSO)-based clustering. DEPSO is a hybrid evolutionary algorithm of the stochastic optimization approach (differential evolution) and the swarm intelligence method (particle swarm optimization) that further increases the search capability and achieves higher flexibility in exploring the problem space. According to the experimental results, we find that the silhouette statistic index stands out in most of the data sets that we examined. Meanwhile, we suggest that users reach their conclusions not just based on only one index, but after considering the results of several indices to achieve reliable clustering structures.
Based on particle swarm optimization (PSO) and artificial bee colony (ABC), a novel hybrid swarm intelligent algorithm is developed in this paper. Two information exchanging processes are introduced to share valuable information mutually between particle swarm and bee colony. Numerical results show that the proposed method is effective and performs better than both PSO and ABC.
A general framework for solving combinatorial optimization problems heuristically by the Ant System approach is developed. The framework is based on the concept of a construction graph, a graph assigned to an instance of the optimization problem under consideration, encoding feasible solutions by walks. It is shown that under certain conditions, the solutions generated in each iteration of this Graph{based Ant System converge with a probability that can be made arbitrarily close to one to the optimal solution of the given problem instance.
Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. In this technology, gene cluster analysis is useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. Many clustering algorithms have been used in the field of gene clustering. This paper proposes a new scheme for clustering gene expression datasets based on a modified version of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, known as the Multi-Elitist QPSO (MEQPSO) model. The proposed clustering method also employs a one-step K-means operator to effectively accelerate the convergence speed of the algorithm. The MEQPSO algorithm is tested and compared with some other recently proposed PSO and QPSO variants on a suite of benchmark functions. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of MEQPSO clustering. The performance of MEQPSO clustering algorithm has been extensively compared with several optimization-based algorithms and classical clustering algorithms over several artificial and real gene expression datasets. Our results indicate that MEQPSO clustering algorithm is a promising technique and can be widely used for gene clustering.