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Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firef...
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Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algori...
Butterfly optimization algorithm is a newcomer in the category of nature inspired optimization algorithm, which is inspired by the food foraging behavior of butterflies. It has shown remarkable performance on various global optimization problems. One of the important parameters which critically affect its performance is sensory modality, which acco...
This paper introduces hybridized monarch butterfly optimization algorithm for solving global optimization problems. Despite of the fact that the monarch butterfly optimization algorithm is relatively new approach, it has already showed great potential when tackling NP-hard optimization tasks. However, by analyzing original monarch butterfly algorit...
Citations
... The firefly algorithm, inspired by the biology proposed by Yang [41], is one of the metaheuristic algorithms. It is based on the pattern of instinctual behavior of fireflies in finding their partners [46][47][48]. The strengths of this algorithm include simplicity, easy implementation, and flexibility [42]. ...
This paper introduces two robust and optimized watermarking techniques designed to protect the copyright of color digital images. These techniques involve embedding a biometric-based digital watermark, generated from an iris image, within the redistributed invariant discrete wavelet transform (RIDWT) domain. In the proposed algorithms, the Y channel of the color image in the YCbCr color space is initially decomposed using RIDWT. Then, the lowest frequency sub-band is partitioned into non-overlapping blocks. Blocks with lower standard deviations are selected to embed each bit of the watermark derived from the iris image. To conceal these watermark bits, two distinct approaches are used. In the first approach, selected blocks undergo a transformation using QR decomposition, while the second approach utilizes Schur decomposition for the same purpose. Before the embedding step, we optimize the embedding strength of the watermarking using the firefly algorithm, striking a balance between robustness and perceptual transparency. Experimental evaluations demonstrate that the proposed algorithms not only deliver good perceptual quality but also effectively resist various geometric and common image processing attacks. Our results underscore the superior robustness of these techniques compared to existing studies. Additionally, both methods maintain an exceptionally low error rate during ownership verification.
... The hybridization of different metaheuristic approaches has demonstrated its advantages in the literature. For example, there were success stories of hybrid DE/Firefly approaches [32,33] as well as hybridization of Firefly with the PSO approach [34][35][36][37]. ...
The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: “Not all marriages have happy endings”.
... This preference stems from the algorithm's robust capabilities in effectively addressing challenging and intricate optimization problems, particularly those characterized by extremeness and complexity. Furthermore, several notable advantages of using the FA for solving complex real-world optimization problems and other related scheduling of unrelated parallel machines tasks include [93], [94]: ...
Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem at hand. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms' performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goals in the context of unrelated parallel machine scheduling. By embracing metaheuristic optimization algorithms, businesses can optimize their scheduling processes while aligning with sustainable principles, leading to improved operational efficiency, cost savings, and a positive contribution to the global sustainability agenda.
... Solving optimization problems can be challenging as they are often highly nonlinear, contain multiple local optima [1], and require dealing with large search spaces. These problems range from scheduling tasks, balancing loads in telecommunication networks [2] to Deep Learning Training [3] and Hyper-parameter Tuning [4]. ...
We propose a new hybrid approach, that we call
History-driven Particle Swarm Optimization-Simulated Annealing
(HdPSO-SA), to improve metaheuristics performance through
collaboration and history-driven methods. Collaboration is performed
using a Self-Adaptive Binary Space Partitioning tree (SABSP
tree) to partition search space and guide the hybrid framework
to the most promising sub-region of a given continuous
problem to solve. The hybrid framework consists of three phases.
In the first phase, the SA-BSP tree is applied in PSO to record
essential information, create the landscape of fitness values, and
partition the search space during exploration. The second phase
consists of a smart controller to learn the SA-BSP maturity
condition to balance exploration and exploitation through HdPSO
and SA, respectively. The proposed smart controller determines
the appropriate step (iteration) for switching from HdPSO to
SA. In the third phase, the search space will be limited to only
the most promising sub-region. Then, the information of the
best solution (fitness value and position) will be given to SA
to exploit the limited search space. The proposed HdPSO-SA
is compared to several metaheuristics on ten well-known unimodal
and multimodal continuous optimization benchmarks. The
results demonstrate the superiority of HdPSO-SA in returning a
good quality solution while reducing the execution time.
... FFA is a very effective engineering algorithm due to its ability to resolve optimization issues even in dynamic domains. In applied mathematics, this algorithm works with simple mathematical logic [25]. Global and local optimization can be found simultaneously because FFA works based on global communication between fireflies in optimizing, especially by using real random values. ...
Not only getting the optimal solution of a problem, embedding the algorithm on the microcontroller is also expected to work optimally without burdening the system and fast response. Getting a microcontroller specification that matches the complexity of an algorithm is necessary so that the system can execute the algorithm perfectly. Values for the basic parameters of optimization algorithms inspired by nature such as the firefly algorithm (FFA) which are interpreted into variables greatly affect the performance of the microcontroller in obtaining the expected optimal solution. The observed performance of the Arduino Uno microcontroller in running the FFA includes execution time and memory capacity required to obtain optimal values based on changes in absorption coefficient, random parameters, iterations, and population. Changes in the absorption coefficient and random parameters affect the optimal value but do not significantly affect the execution time and memory capacity of Arduino Uno. Iteration changes greatly affect execution time and population changes most affect the performance of Arduino Uno. With a dynamic memory capacity of 2 Kb, the FFA can be run with a maximum range of 50 populations and up to 20 iterations.
... The FA is based on the attraction behaviour of tropical fireflies and the flashing patterns of their idealized behaviour. Since 2010, the FA algorithm has been implemented in different real applications for solving different optimization tasks [42]. The attractiveness of fireflies and variation in light intensity is the important factor of fireflies [43]. ...
K-means clustering is not able to select the right number of clusters of data items having high-dimension. So, for determining the ideal number of clusters, we have combined PCA with the Silhouette and Elbow approaches. Additionally, we have a large number of meta-heuristic swarm intelligence algorithms which is influenced by nature and were previously used to solve the automatic data clustering problem. Firefly offers reliable and effective automatic data clustering. The Firefly algorithm automatically divides the entire population into subpopulations, which slows down the convergence and reduces the of capturing local minima in high-dimensional optimization problems. Thus, for automatic clustering, we demonstrated an improved firefly, i.e., we offered a hybridized K-means with an ODFA model. The experimental section displays the results and graphs for the Silhouette, Elbow, and Firefly algorithms.
... It implies that FA is satisfying at exploration as well as diversification. 57 On the other hand, BAT utilizes simple concepts and structure, maintains the diversity of solutions in the population, has a fast convergence rate due to automatically zooming into a region of promising solutions, and has good exploitation proficiency. However, it requires an enhanced control strategy to switch between exploitation and exploration at the right moment and suffers from a lack of sufficient exploration ability. ...
With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.
... (1) Traditional gradient-based optimization methods are prone to fall into the trap of local optima. When the gradient value is small, the method tends to terminate the loop early [12,13]; ...
... However, it suffers from premature convergence in most cases and the possibility to get trapped in local minima is very high as well as its global search capability is restricted [13], [17]. Limitations of FFA can be overcome by combining it either with other meta-heuristic algorithms [18] or with the Lévy flight random walk [19]. ...
In Wireless Sensor Networks (WSNs), nodes have minimal energy autonomy. Accordingly, designing routing protocols that reduce the total network energy usage is one of the most challenging tasks in this field. Recent deployments of energy-efficient routing protocols for WSNs have used clustering mechanisms. In this regard, choosing optimal placements of cluster heads is an NP-hard problem that can be solved using a variety of biomimetic meta-heuristic algorithms. The Firefly Algorithm (FFA) is considered as one of the most promising and effective algorithms already used for addressing nonlinear optimization problems in general, and the energy-aware clustering for WSNs in particular. However, when solving complex optimization problems, FFA has a high risk of becoming trapped in the local optimum. Since the randomization operator plays a crucial role in updating particle positions and enhancing its global search (exploration) and convergence (exploitation) behaviors, Lévy flight-based random walk has been deployed to improve the firefly algorithm's searching capability and prevent it from the premature convergence, thereby preventing it from trapping in the local optimum. This paper proposes an Adaptive Lévy-Flight Firefly Algorithm-based Protocol (ALFFAP) to increase the energy efficiency in WSN. MATLAB 2018a is used to simulate and assess the proposed approach, and its performance is compared to that of the classical Firefly algorithm (FFA)-based clustering protocol, LEACH, and LEACH-C. ALFFAP outperforms other protocols regarding the number of surviving nodes, total energy consumption, death of the first node, death of half node, death of the last node, stability period, and the number of data packets forwarded to the Base Station.
... Hybrid [27] 2013 PSO+GA Particle Swarm Optimization + Genetic Algorithm (Continued) [28] 2018 HFPSO Firefly + Particle Swarm Optimization [29] 2020 HHOSA Harris Hawks + Simulated annealing [30] 2021 GWOHHO Grey wolf + Harris Hawks [31] 2022 AOAAO Aquila + Arithmetic optimization Some hybrid algorithms have been reported to outperform native algorithms in feature selection. Zhang et al. [32] proposed a hybrid Aquila Optimizer with Arithmetic Optimization Algorithm (AO-AOA), which provides faster convergence in the best global search and produced better results than native methods. Wang et al. [33] combined the merits of both Differential Evolution (DE) and Firefly algorithm (FA). ...
Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud. The efficiency of BinHHO is analyzed with other popular MH algorithms. The BinHHO has achieved the highest accuracy of 99.95% and demonstrates a more significant positive effect on the performance of the proposed model.
