Abstract
Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Levy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Levy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.
Highlights
IntroductionWith the expansion of tools and soft computing methods, the community is facing many real-life tasks that need to feasible solutions using mathematical models [1]–[7]
With the expansion of tools and soft computing methods, the community is facing many real-life tasks that need to feasible solutions using mathematical models [1]–[7].The associate editor coordinating the review of this manuscript and approving it for publication was Hao Luo .metaheuristic methods (MAs) and machine learning models are effective substitute techniques for tackling practical problems [8]–[11]
WORKS The CLSCA method presented in this paper can effectively improve the performance of the original sine cosine algorithm (SCA) method
Summary
With the expansion of tools and soft computing methods, the community is facing many real-life tasks that need to feasible solutions using mathematical models [1]–[7]. The associate editor coordinating the review of this manuscript and approving it for publication was Hao Luo. metaheuristic methods (MAs) and machine learning models are effective substitute techniques for tackling practical problems [8]–[11]. Inspired by nature or physical problems, numerous intelligent algorithms have been widely used to solve optimization problems [12]–[16]. These intelligent algorithms often show better results than traditional gradient-based algorithms [17].
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