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A comprehensive survey of sine cosine algorithm: variants and applications
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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.
This paper aims to minimize the generation cost of a low voltage (LV) grid-connected microgrid system using a novel hybrid whale optimization algorithm (WOA)- Sine cosine algorithm (SCA) optimization technique. Three different methods of electricity market prices are formulated in turns and the generation cost is evaluated for each method to sort out the best among them which yields the least generation cost of the same microgrid system. Both active and passive participation of grid are studied to acknowledge its importance. Numerical results show that the time of usage (TOU) method of electricity market pricing proved to be the most convenient and economic method for cost analysis of the system. The generation cost was at its maximum when the electricity price was a constant value. Also during the TOU method of electricity pricing, a 15% surge in the price was realized when the grid was passively participating in supplying power to the system. Figures and statistical data aids the fact that proposed hybrid WOASCA outperformed WOA in consistently providing a robust and superior quality result.
Feature selection (FS) is a real-world problem that can be solved using optimization techniques. These techniques proposed solutions to make a predictive model, which minimizes the classifier's prediction errors by selecting informative or important features by discarding redundant, noisy, and irrelevant attributes in the original dataset. A new hybrid feature selection method is proposed using the Sine Cosine Algorithm (SCA) and Genetic Algorithm (GA), called SCAGA. Typically, optimization methods have two main search strategies; exploration of the search space and exploitation to determine the optimal solution. The proposed SCAGA resulted in better performance when balancing between exploitation and exploration strategies of the search space. The proposed SCAGA has also been evaluated using the following evaluation criteria: classification accuracy, worst fitness, mean fitness, best fitness, the average number of features, and standard deviation. Moreover, the maximum accuracy of a classification and the minimal features were obtained in the results. The results were also compared with a basic Sine Cosine Algorithm (SCA) and other related approaches published in literature such as Ant Lion Optimization and Particle Swarm Optimization. The comparison showed that the obtained results from the SCAGA method were the best overall the tested datasets from the UCI machine learning repository.
Blasting of rocks has intrinsic environmental impacts such as ground vibration, which can interfere with the safety of lives and property. Hence, accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature. Therefore, there is need to consider some robust predictive models for accurate prediction results. Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neu-ral network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries. The input parameters into the models are the distance from the point of blasting to the point of measurement (D), the weight of charge per delay (W), rock density (q), and the Schmidt rebound hardness (SRH) value while peak particle velocity (PPV) is the targeted output. 100 datasets were used in developing the proposed models. The performance of the proposed models was examined using the coefficient of determination (R 2) and error analysis. The R 2 values obtained for the GEP, ANFIS, and SCA-ANN models are 0.989, 0.997, and 0.999, respectively, while their errors are close to zero. The proposed models are compared with an empirical model and are found to outperform the empirical model. Ó 2020 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions.
Cross layer resource allocation in the wireless networks is approached traditionally either by communications networks or information theory. The major issue in networking is the allocation of limited resources from the users of network. In traditional layered network, the resource are allocated at medium access control (MAC) and the network layers uses the communication links in bit pipes for delivering the data at fixed rate with the occasional random errors. Hence, this paper presents the cross-layer resource allocation in wireless network based on the proposed social-sine cosine algorithm (SSCA). The proposed SSCA is designed by integrating social ski driver (SSD) and sine cosine algorithm (SCA). Also, for further refining the resource allocation scheme, the proposed SSCA uses the fitness based on energy and fairness in which max-min, hard-fairness, proportional fairness, mixed-bias and the maximum throughput is considered. Based on energy and fairness, the cross-layer optimization entity makes the decision on resource allocation to mitigate the sum rate of network. The performance of resource allocation based on proposed model is evaluated based on energy, throughput, and the fairness. The developed model achieves the maximal energy of 258213, maximal throughput of 3.703, and the maximal fairness of 0.868, respectively.
The sine cosine algorithm (SCA) is a recently developed meta-heuristic algorithm for solving global optimization problems. It has shown excellent performance in meta-heuristic algorithms. But this algorithm also has shortcomings such as low accuracy, easy to fall into a local solution, and slow convergence speed. Aiming at these deficiencies of the SCA, a modified sine cosine algorithm with teacher supervision learning (TSL-SCA) for global optimization is proposed. First, the teacher supervision strategy can guide the population convergence and accelerate the convergence speed. Second, individuals perform reflective learning after the standard SCA position is updated, which can effectively prevent individuals from stagnating in the evolutionary process and increase population diversity. In addition, a hybrid inverse learning method is proposed. It can not only enhance the ability of finding global optimal solution and increase population distributivity, but also balance the exploration and exploitation capabilities. Differential evolution algorithm (DE), particle swarm optimization (PSO), cuckoo search (CS) algorithm, moth-flame optimization (MFO), whale optimization algorithm (WOA), Teaching-Learning-Based Optimization (TLBO), SCA and TSL-SCA are selected for simulation experiment to solve 33 benchmark optimization problems. The experimental results show that the TSL-SCA can significantly enhance the optimization accuracy and convergence speed. Furthermore, the effectiveness of the proposed method is examined by solving analog circuit fault diagnosis of filter circuit examples.
This paper proposes a novel strategy, atom search sine cosine algorithm (ASSCA) for multi-focus image fusion. Here, the discrete wavelet transform (DWT) is adapted for transforming images into sub-bands. The fusion is carried out using a fusion rule based on weighting criteria that uses two attributes, Renyi entropy and the proposed ASSCA. Entropy discovers the entropy fusion factor considering the assessed entropy from the source image. Further, an optimization strategy, ASSCA is developed by integrating atom search optimization and sine cosine algorithm for precise selection of fusion factor. The output obtained from the fusion undergoes inverse discrete wavelet transform to obtain the resultant fused image. The proposed DWT + ASSCA + Renyi entropy outperformed other methods with maximal mutual information of 1.492, maximal peak signal-to-noise ratio of 40.625 dB, minimal root mean-squared error of 7.651, maximum correlation coefficient of 0.988, and minimum deviation index of 1.146.
The Sine Cosine Algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance of the SCA against similar algorithms.
In this paper, a quantum-based sine cosine algorithm, named as Q-SCA, is proposed for solving general systems of nonlinear equations. The Q-SCA hybridizes the sine cosine algorithm (SCA) with quantum local search (QLS) for enhancing the diversity of solutions and preventing local optima entrapment. The essence of the proposed Q-SCA is to speed up the optimum searching operation and to accelerate the convergence characteristic. The proposed Q-SCA works in twofold: firstly, an improved version of SCA based on tuning the search space around the destination solution dynamically, so that the search space is shrunken gradually as the optima are attained. In addition, a new mechanism to update the solutions is introduced using bidirectional equations. Secondly, QLS is incorporated to improve the quality of the obtained solutions by the SCA phase. By this methodology, the proposed Q-SCA can achieve high levels of exploration/exploitation and precise stable convergence to high-quality solutions. The performance of the proposed algorithm is assessed by adopting twelve systems of nonlinear equations and two electrical applications. Furthermore, the proposed Q-SCA algorithm is applied on expensive large-scale problems including CEC 2017 benchmark and realistic optimal power dispatch (OPD) to confirm its scalability. Experimental results affirm that the Q-SCA is performs steadily, and it has a promising overall performance among several compared algorithms.
This article presents a new image segmentation approach based on the principle of clustering optimized by the meta-heuristic algorithm namely: SCA (Algorithm Sinus Cosine). This algorithm uses a mathematical model based on trigonometric functions to solve optimization problems. Such an approach was developed to solve the drawbacks existing in classic clustering techniques such as the initialization of cluster centers and convergence towards the local optimum. In fact, to obtain an “optimal” cluster center and to improve the image segmentation quality, we propose this technique which begins with the generation of a random population. Then, we determine the number of clusters to exploit. Later, we formulate an objective function to maximize the interclass distance and minimize the intra-class distance. The resolution of this function gives the best overall solution used to update the rest of the population. The performances of the proposed approach are evaluated using a set of reference images and compared to several classic clustering methods, like k-means or fuzzy c-means and other meta-heuristic approaches, such as genetic algorithms and particle swarm optimization. The results obtained from the different methods are analyzed based on the best fitness values, PSNR, RMSE, SC, XB, PC, S, SC, CE and the computation time. The experimental results show that the proposed approach gives satisfactory results compared to the other methods.
This paper proposes a fractional-order (FO) based PD structured static synchronous series compensator (SSSC) to improve power system stability. The controller parameters are tuned by using the modified sine cosine algorithm (MSCA) which is developed in this paper. The controller performance in a single-machine infinite bus system is verified with different loading conditions. Furthermore, to demonstrate the effectiveness of the developed MSCA algorithm the MATLAB/ SIMULINK results obtained are compare with the results obtained with genetic algorithm (GA), particle swarm optimization (PSO), and conventional SCA. The comparison between MSCA with those of other algorithms confirms its dominance in the current context.
The sine cosine (SC) algorithm was very popular for scientists and engineers since it was raised in 2016, it was an efficient, simple algorithm that could be applied in real problems. In this paper, an improved version of the SC algorithm with multiple updating ways for individuals in swarms during iterations was proposed. Inspired by the slime mould (SM) algorithm, the algorithm was re-constructed and consequently, the individuals would update their positions in three ways, parts of them would be reinitialized, parts of them following the same traditional ways, and parts of them would explore further according to their own historical trajectories. Simulation experiments were carried out and the averaged results proved that the improved version would perform better, faster, and steadier than the original one.
Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine–cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine–cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine–cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.
Crow Search Algorithm (CSA) is a recent swarm intelligence optimization algorithm inspired by the social intelligent behavior of crows for hiding food. It has been widely used to solve a large variety of optimization problems in several fields and areas of research and has proved its efficiency compared to several state-of-the-art optimization algorithms available in the literature. This paper presents a comprehensive overview of Crow Search Algorithm and its new variants categorized into modified and hybridized versions. It also describes the several applications of CSA in various domains such as feature selection, image processing, scheduling, economic dispatch, distributed generation, and other engineering problems. In addition, the paper suggests some interesting research areas related to CSA enhancement, CSA hybridization, and possible new applications.
Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.
Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems.
One of the important directions of the current research trend in distribution network planning in the prevailing smart grid scenario, is to explore various possibilities to enhance the performance of these networks without expanding the existing infrastructure. This paper proposes an enhanced sine–cosine algorithm (ESCA) to obtain an optimally planned system by simultaneous incorporation of network reconfiguration (NR) and DG allocation. In the proposed algorithm, the SCA is enhanced with neighborhood search strategy and self-adapting levy mutation strategy to ensure proper balance between exploration and exploitation during different reconfiguration phases. A multi-objective function is formulated considering the reduction of total real power loss and annual operation costs with suitable weights without violating the system operating constraints. The proposed algorithm is successfully experimented on 33- and 69-bus distribution system with four distinct scenarios of NR and DG allocation, and its performance assessment is based on technical (total system active power loss index, overall voltage stability index and voltage profile improvement index), economic (total system operation cost index) and reliability (expected energy not supplied index) indices. As the computation of reliability index adds complexity to the problem, a graph theory-based algorithm is proposed for its accurate calculation. The obtained results showed the effectiveness of ESCA for solving simultaneous NR and DG allocation problem over other competitive algorithms, and its robustness is confirmed through a detailed statistical analysis such as plotting of box plots, normality checking and two nonparametric tests, namely Friedman ANOVA and Wilcoxon signed rank tests.
This study proposed an improved sine–cosine algorithm (SCA) for global optimization tasks. The SCA is a meta-heuristic method ground on sine and cosine functions. It has found its application in many fields. However, SCA still has some shortcomings such as weak global search ability and low solution quality. In this study, the chaotic local search strategy and the opposition-based learning strategy are utilized to strengthen the exploration and exploitation capability of the basic SCA, and the improved algorithm is called chaotic oppositional SCA (COSCA). The COSCA was validated on a comprehensive set of 22 benchmark functions from classical 23 functions and CEC2014. Simulation experiments suggest that COSCA’s global optimization ability is significantly improved and superior to other algorithms. Moreover, COSCA is evaluated on three complex engineering problems with constraints. Experimental results show that COSCA can solve such problems more effectively than different algorithms.
Hybrid active power filter (HAPF) is a novel technique of harmonic filter which combines superiorities of both active and passive filters. However, extracting appropriate parameters of the HAPF, including active filter gain, passive inductive, and capacitive reactance within a constraint space is still a challenging task. To obtain more accurate parameters of HAPF, this paper proposed a new population-based algorithm named ASC-MFO. In ASC-MFO, the swarm is divided into two sub-swarms, i.e., exploitation group and the exploration group. The exploitation group adopts the SFM in the MFO algorithm to enhance the exploitation ability, while the exploration group utilizes the SCM in the SCA algorithm to emphasize exploration. Besides, a personal best flame generation (PFG) strategy and a hybrid exemplar generation (HEG) strategy are developed for the exploitation group and the exploration group to further enhance the exploitation ability and the exploration ability of the two subgroups, respectively. Moreover, an adaptive strategy is proposed to automatically resize the population number of two sub-swarms during the iterative process, which can precisely balance the exploration and exploitation ability between groups in every single generation. The proposed ASC-MFO is applied to design the two most commonly used topologies of the HAPF, where each topology contains four actual cases. Comprehensive experimental results demonstrate that ASC-MFO obtains an excellent performance among those well-established algorithms, especially in the aspect of accuracy and reliability.
The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution systems in the recent deregulation caused by continuous load demand and transmission system contingency. In this perspective, a new multi-objective sine cosine algorithm is proposed for optimal DG allocation in radial distribution systems with minimization of total active power loss, maximization of voltage stability index, minimization of annual energy loss costs as well as pollutant gas emissions without violating the system and DG operating constraints. The proposed algorithm is enhanced by incorporating exponential variation of the conversion parameter and the self-adapting levy mutation to increase its performance during different iteration phases. The contradictory relationships among the objectives motivate the authors to generate an optimal Pareto set in order to help the network operators in taking fast appropriate decisions. The proposed approach is successfully applied to 33-bus and 69-bus distribution systems under four practical load conditions and is evaluated in different two-objective and three-objective optimization cases. The effectiveness of the algorithm is confirmed by comparing the results against other well-known multi-objective algorithms, namely, strength Pareto evolutionary algorithm 2, non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. The quality of Pareto fronts from different multi-objective algorithms is compared in terms of certain performance indicators, such as generational distance, spacing metric and spread metric (\({\varDelta }\)), and its statistical significance is verified by performing Wilcoxon signed rank test.
Phising is a dangerous social engineering cyber crime that aims to steal personal information of the user by sending spoofed emails. There are several applications of machine learning used to detect phising attacks. However, when a security model is built, the attacker tries to breach through it. Thus the question of optimal prediction of phising attack is still unanswerable. In this work, we integrated sine cosine algorithm, a metaheuristic population based technique with K-Nearest Neighbor (SCAK-NN) for predicting phising attacks optimally. The proposed SCAK-NN is compared with Decision Trees and Naïve Bayes. The results are satisfactory when comparing with various performance metrics such as accuracy, F-measure, True positive Rate (TPR), False Positive Rate (FPR) and Mean Absolute error (MAP) to hold that SCAK-NN is better than Decision Trees and Naïve Bayes.
The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train mutilayer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional large-scale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.
The fruit fly optimization algorithm (FOA) is a well-regarded algorithm for searching the global optimal solution by simulating the foraging behavior of fruit flies. However, when solving high dimensional mathematical and practical application problems, FOA is not competitive in convergence speed, and it may quickly fall into the local optimum. Therefore, in this paper, an enhanced fruit fly optimizer, termed SCA_FOA, is developed by introducing the logic of the sine cosine algorithm (SCA). Specifically, in the process of searching for food utilizing the osphresis organ, the individual fruit fly adopts the way inspired by the SCA to fly outward or inward to find the global optimum. A comprehensive set of 28 benchmark functions were used to measure the exploitation and exploration abilities of the proposed SCA_FOA. The results demonstrate that SCA_FOA is superior to other competitive algorithms. Moreover, 10 practical problems from IEEE CEC 2011, three engineering problems, three shifted and asymmetrical functions, and optimization problems of kernel extreme learning machines (KELM) were also solved, effectively. The results and observations indicate that not only the proposed SCA_FOA can be used for simulated problems as a very efficient method, but also it can be employed for real-world applications. Visit: https://aliasgharheidari.com/
Abstract: Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor
nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an
essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the
authors propose a node localisation scheme based on a sine–cosine algorithm. The proposed scheme addresses the flip
ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme
is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the
proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and
the computation time.
COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences. The huge time consumption of the standard global alignment algorithm is its main limitation especially for sequences with huge lengths. This work proposed a fast global alignment tool (G-Aligner) based on meta-heuristic algorithms that estimate similarity measurements near the exact ones at a reasonable time with low cost. The huge length of sequences leads G-Aligner based on standard Sine-Cosine optimization algorithm (SCA) to trap in local minima. Therefore, an improved version of SCA was presented in this work that is based on integration with PSO. Besides, mutation and opposition operators are applied to enhance the exploration capability and avoiding trapping in local minima. The performance of the improved SCA algorithm (SP-MO) was evaluated on a set of IEEE CEC functions. Besides, G-Aligner based on the SP-MO algorithm was tested to measure the similarity of real biological sequence. It was used also to measure the similarity of the COVID-19 virus with the other 13 viruses to validate its performance. The tests concluded that the SP-MO algorithm has superiority over the relevant studies in the literature and produce the highest average similarity measurements 75% of the exact one.
With the significant growth of multiprocessor systems (MPS) to deal with complex tasks and speed up their execution, the energy generated as a result of this growth becomes one of the significant limits to that growth. Although several traditional techniques are available to deal with this challenge, they don’t deal with this problem as multi-objective to optimize both energy and makespan metrics at the same time, in addition to expensive cost and memory usage. Therefore, this paper proposes a multi-objective approach to tackle the task scheduling for MPS based on the modified sine-cosine algorithm (MSCA) to optimize the makespan and energy using the Pareto dominance strategy; this version is abbreviated as energy-aware multi-objective MSCA (EA-M2SCA). The classical SCA is modified based on dividing the optimization process into three phases. The first phase explores the search space as much as possible at the start of the optimization process, the second phase searches around a solution selected randomly from the population to avoid becoming trapped into local minima within the optimization process, and the last searches around the best-so-far solution to accelerate the convergence. To further improve the performance of EA-M2SCA, it was hybridized with the polynomial mutation mechanism in two effective manners to accelerate the convergence toward the best-so-far solution with preserving the diversity of the solutions; this hybrid version is abbreviated as EA-MHSCA. Finally, the proposed algorithms were compared with a number of well-established multi-objective algorithms: EA-MHSCA is shown to be superior in most test cases.
In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm, and sine-cosine algorithm are used for shape optimization. The results show the ability of the sine-cosine optimization algorithm to optimize automobile components in the industry.
This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm (AMVO-SCA) for identifying the continuous-time Hammerstein system. In this paper, two modifications were employed on conventional MVO. Our first modification was an average design parameter updating mechanism to solve the local optima issue in conventional MVO. The essential feature of the average design parameter updating mechanism is that it helps any trapped design parameter to jump out from the local optima region and continue a new search track. Meanwhile, the second modification was the hybridization of MVO with SCA, to improve the poor searching capability of the conventional MVO. In particular, the searching capability of MVO was improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA), which will balance the exploration and exploitation processes. The proposed AMVO-SCA based method was then used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. Note that the structure of the linear and nonlinear subsystems was assumed to be known. A continuous-time linear subsystem was also considered in this study, while there were a few methods that utilize such models. The efficiency of the AMVO-SCA based method is illustrated by three numerical examples, including a double pendulum overhead crane model. Furthermore, various nonlinear subsystems were used in those examples, such as the quadratic and hyperbolic functions. The numerical results analysis was observed with respect to the convergence curve of the fitness function (in terms of the quadratic output estimation error), the parameter deviation index, the estimated nonlinear function plot, the bode plot of the linear subsystem and the Wilcoxon's rank test at a significance level of 5%. The results showed that the method was efficient in identifying both the Hammerstein model subsystems, in terms of the quadratic output estimation error and parameter deviation index. The obtained results also confirmed that the proposed AMVO-SCA based method provided better solutions compared to other optimization methods including Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), MVO and SCA.
Heterogeneous fixed fleet vehicle routing problem is a real-life variant of classical VRP, which is a well-established NP-hard optimization problem. In this paper, a hybrid approach based on sine cosine algorithm and particle swarm optimization, namely HSPS, is proposed to solve heterogeneous vehicle routing problem. This hybridization incorporates the strength of both the algorithms for solving this variant. It works in two stages. In first stage, sine cosine algorithm is used to examine the unexplored solution space, and then in next stage, particle swarm optimization is used to exploit the search space. The proposed algorithm has been tested and compared with other algorithms on several benchmark instances. The numerical and statistical results demonstrate that the proposed hybrid is competitive with other existing hybrid algorithms in solving benchmarks with faster convergence rate.
The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.
Sine-cosine algorithm (SCA) has found a widespread application in various engineering optimization problems. However, SCA suffers from premature convergence and insufficient exploitation. Cylindricity error evaluation is a typical engineering optimization problem related to the quality of cylindrical parts. A hybrid greedy sine-cosine algorithm with differential evolution (HGSCADE) is developed in this paper to solve optimization problems and evaluate cylindricity error. HGSCADE integrates the SCA with the opposition-based population initialization, the greedy search, the differential evolution (DE), the success history-based parameter adaptation, and the Levy flight-based local search. HGSCADE is tested on the CEC2014 benchmark functions and is employed in cylindricity error evaluation. The results show the superiority of HGSCADE to other state-of-the-art algorithms for the benchmark functions and cylindricity error evaluation.
To solve global optimization problems, this paper proposed a novel improved version of sine cosine algorithm — the dimension by dimension dynamic sine cosine algorithm (DDSCA). In the update equation of sine cosine algorithm (SCA), the dimension by dimension strategy evaluates the solutions in each dimension, and the greedy strategy is used to form new solutions after combined them with other dimensions. Moreover, in order to balance the exploration and exploitation of SCA, a dynamic control parameter is designed to modify the position equation of this algorithm. To evaluate the effectiveness of DDSCA in solving global optimization problems, it is compared with state-of-art algorithms and modified SCA on 23 benchmark functions. The experimental results reveal the DDSCA has better robustness and efficiency. The IEEE CEC2010 large-scale functions are selected to solve high-dimensional optimization problem, the results show that the performance of the DDSCA is better than other algorithms. In addition, five engineering optimization problems are also verified the effectiveness of the DDSCA. The results of accuracy and speed show that the improved sine cosine algorithm (DDSCA) is competitive in solving global optimization problems.
Sine cosine algorithm (SCA) is an emerging meta-heuristic method for the complicated global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. To improve the SCA performance, this paper develops a modified sine cosine algorithm coupled with three improvement strategies, where the quasi-opposition learning strategy is used to balance global exploration and local exploitation; the random weighting agent produced by multiple leader solutions is integrated into the agent’s evolution equation to improve the convergence rate; the adaptive mutation strategy is designed to increase the swarm diversity. The proposed method is compared with several famous evolutionary methods on 12 classical test functions, 24 CEC2005 composite functions and 30 CEC2017 benchmark functions. The results show that the proposed method outperforms several control methods in both solution quality and convergence rate. Then, the long-term operation optimization of multiple hydropower reservoirs in China is chosen to testify the engineering practicality of the developed method. The simulation results indicate that in different scenarios, the proposed method can produce satisfying scheduling schemes with better objective values compared with several existing evolutionary methods. Hence, a novel optimizer is provided to handle the complicated engineering optimization problem.
Nature-inspired metaheuristic algorithms along with their improved and hybrid versions have been gaining intrinsic popularity in solving nonlinear constrained complex real-world problems. On this presentation, a new hybrid butterfly optimization algorithm (BOA), viz. BOSCA combined with sine cosine algorithm (SCA) is suggested to develop a balanced yet powerful optimization technique through enhancing and stabalizing the global exploration and local exploitation ability. In this, metaheuristic hybridization is done in such a way to get both the exploration and exploitation phases for each of the butterfly with sufficient chance to improvise each solution. To prove the efficiency and robustness of the developed BOSCA, it has been applied to solve twenty-five classical benchmark functions. A comparative study has been done by taking some of the popular algorithms in available in literature and this developed algorithm is found to be superior to the compared algorithms. Again to validate its efficiency in real-world problems, it has been applied to two real-world problems; One is gas transmission compressor design problem and another is optimal capacity of gas production facilities. Results of these real-world problems have been compared to that of some other algorithms and the proposed method found to be superior in real-world optimization problems also.
The Meta-heuristic algorithm has become an effective solution to global optimization problems. Recently, a new meta-heuristic algorithm called sine-cosine algorithm (SCA) search algorithm is proposed, which uses the characteristics of sine-cosine trigonometric function in mathematical formulas to solve the optimal solution of the problem to be optimized. This paper presents a new variant of the SCA algorithm named Bare bones Sine Cosine Algorithm (BBSCA), which improves the exploitation ability of the solution, reduces the diversity spillover in the classical SCA search equation, and keeps the diversity of the solution very well. The proposed method uses Gaussian search equations and exponential decrement strategies to generate new candidate individuals, which use the valuable information hidden in the best individuals to guide the population to move in a better direction. At the same time, the greedy selection mechanism is adopted for the newly generated solution, which makes full use of the previously searched information to improve the individual's search ability. To evaluate the effectiveness in solving the global optimization problems, BBSCA has been tested on classic set of 23 well-known benchmark functions, standard IEEE CEC2014 and CEC2017 benchmark functions, and compared with several other state-of-the-art SCA algorithm variants. At the end of the paper, the performance of design algorithm BBSCA is also tested on classical engineering optimization problems. The numerical and simulation experimental results indicate that the proposed method can improve the performance of the algorithm and generate better statistical significance solutions in real-life global optimization problems.
The accurate determination of postblast ore boundaries can significantly help to control ore loss and dilution in opencast mines. Determining the boundaries is difficult using methods other than direct and expensive blast-induced rock movement monitoring, so many mines directly use the preblast ore boundary to guide the shovel. A new postblast ore boundary determination method using a soft computing technique and stochastic modelling method is proposed. Based on a case study and performance comparison, a high-precision hybrid metaheuristic model combined with the sine cosine algorithm and random forest technique (SCA-RF) was developed and used in a Monte Carlo simulation to analyse the probability distribution and parameter sensitivity. Mining engineers can obtain a more accurate postblast ore boundary by moving the preblast ore boundary toward the free face by a certain distance after considering the probability distribution of blast-induced rock movement, which is significantly better than using the preblast ore boundary.
Herein, in a grid-integrated photovoltaic system the maximum power point is reached by adjusting the on-time of the boost converter set by modified sine cosine algorithm (MSCA) for partial shading condition. The results are contrasted with perturb and observe method and five contemporary optimizations. The suggested application of MSCA enables ripple-free power, voltage and current by optimization in lesser time than others and exhibits superior efficiency for different configurations as compared to recent related techniques in this area.
The sine cosine algorithm (SCA) is a new population-based stochastic optimization algorithm, utilizing the oscillating property of the sine cosine function to balance the exploration and exploitation performance of SCA. A hybrid sine cosine algorithm based on the optimal neighborhood and quadratic interpolation strategy (QISCA) was proposed to overcome the shortcoming of updating the population guided by the global optimal individual in the sine cosine algorithm. The new algorithm uses a Stochastic Optimal Neighborhood for neighborhood updates, and it adopts a Quadratic Interpolation curve for individual updates. In addition, QISCA incorporates Quasi-Opposition Learning strategies to enhance the population’s global exploration capabilities, and improves the convergence speed and accuracy. The two simulation experiments of 23 benchmark functions and 30 latest CEC2017 test functions show that the new algorithm can better coordinate the exploration and exploitation capabilities and improve the global optimization ability, compared with the other improved sine cosine algorithm and the representative stochastic optimization algorithm. The three representative engineering problems validate the effectiveness of the new algorithm to solve practical problems.
Recently, the multiple hydropower reservoirs operation optimization is attracting rising concerns from researchers and engineers since it can not only improve the utilization efficiency of water resource but also increase the generation benefit of hydropower enterprises. Mathematically, the reservoir operation problem is a typical multistage constrained optimization problem coupled with numerous decision variables and physical constraints. Sine cosine algorithm (SCA), a new swarm-based method, has the merits of clear principle and easy implementation but suffers from the premature convergence and falling into local optima. To improve the SCA performance, this paper proposes an adaptive sine cosine algorithm (ASCA) where the elite mutation strategy is used to increase the population diversity, the simplex dynamic search strategy is used to enhance the solution quality, while the neighborhood search strategy is used to improve the convergence rate. The simulations of 25 test functions show that ASCA outperforms several existing methods in both convergence rate and solution quality. The results of a real-world hydropower system in China demonstrate that ASCA betters the SCA method with about 18.56 ×10⁸kW·h increase in power generation. Thus, the main contribution of this study is to provide an effective optimizer for multiple hydropower reservoirs operation.
Real-world optimization problems demand an algorithm which properly explores the search space to find a good solution to the problem. The sine cosine algorithm (SCA) is a recently developed and efficient optimization algorithm, which performs searches using the trigonometric functions sine and cosine. These trigonometric functions help in exploring the search space to find an optimum. However, in some cases, SCA becomes trapped in a sub-optimal solution due to an inefficient balance between exploration and exploitation. Therefore, in the present work, a balanced and explorative search guidance is introduced in SCA for candidate solutions by proposing a novel algorithm called the memory guided sine cosine algorithm (MG-SCA). In MG-SCA, the number of guides is decreased with increase in the number of iterations to provide a sufficient balance between exploration and exploitation. The performance of the proposed MG-SCA is analysed on benchmark sets of classical test problems, IEEE CEC 2014 problems, and four well known engineering benchmark problems. The results on these applications demonstrate the competitive ability of the proposed algorithm as compared to other algorithms.
Widespread growth of multimedia content distribution can increase the cost and time of the content distribution. The multimedia services are stored by the service provider and the user is provided based on their demand. Basically, the network user number increases quickly, and the response time for huge numbers of users also increases rapidly. Therefore, the need of service cannot be reached. Initially, we solicited notable extraction technique to collect the interest features of user. The adjacent region and similar service interests of users are divided into service user and nonservice user. Therefore, the coherent utility value is suggested to the user evaluation procedure, so the combination of different users experience character is needed to calculate the integrated utility value. Hence, the users experience characteristics are derived by presentation of physical user, behavior of selfish user and character of the user. Consequently, we minimized the content distribution cost and time with crossover-based sine cosine algorithm (CSCA). The proposed CSCA was established for the selection of user service number. The experimental results of proposed method can decrease the multimedia user cost and improve the performance of multimedia content.
The article contributes a research work on optimal designing a Fractional Order-Multistage PD/(1+PI) controller (FO-Multistage) for controlling frequency of an islanded AC Microgrid under various uncertainties. The renewable energy based proposed Microgrid system is configured with the penetration of numbers of distributed generation (DG) systems. However, the large uncertainties and low inertia of most DG system results fluctuation of frequency in islanded microgrid system. To maintain nominal frequency, the article proposes a robust FO-Multistage controller to constitute secondary control loop in the microgrid system. An Improved-Sine Cosine algorithm (i-SCA) is suggested to optimal design the robust FO-Multistage controller under various uncertainties.The system performance is also verified under stochastic model generated wind and solar power uncertainties. The viability of proposed approaches are demonstrated through a suitable comparative analysis over some conventional approaches. It has been investigated that, the system performance has been improved gracefully with proposed i-SCA optimized FO-Multistage controller.
This paper proposes a novel methodology for the detection of partial shading conditions in photovoltaic (PV) arrays based on the experience gained in the preliminary step of the detection algorithm. In the first stage of the problem, the periodic partial shading detection (PSD) problem is solved to detect the periodic partial shading condition (PSC) and to determine the optimal number of executing point of MPPT algorithm during PSC. The second stage of the PSD problem solves the maximum power point tracking (MPPT) problem to extract the maximum power from PV array at the executing point. The PSD problem is solved using the sine cosine algorithm (SCA) and to determine the global maximum operating point under various partial shading conditions, the improved sine cosine algorithm (ISCA) is proposed. The proposed method is guaranteed to find periodic shade and the global maximum operating point, avoiding the local operating point obstacle. Using MATLAB, the algorithm is implemented and tested in a simulation model. An experimental 2 kW PV system is developed to validate the operating point of the PV system under various partial shading patterns. The results demonstrate that the proposed algorithm outperforms the genetic algorithm and particle swarm optimization-based partial shading detection problem.