A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems

Jianxun Liu, Jinfei Shi, Fei Hao and Min Dai

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 201, issue C, 23-48

Abstract: Whale optimization algorithm (WOA) is a meta-heuristic algorithm. In process of optimizing single-peak problem and multi-peak problem, it has the problems of weak exploration ability, poor convergence behavior and easy to fall into local optimum (LO). To solve these problems, we analyze WOA from the two aspects of global exploration efficiency and convergence characteristics. Correspondingly, the population redistribution and convergent adaptive weighting strategy are proposed. The population redistribution strategy can improve the space utilization of WOA in search process, enhance its exploration efficiency and prevent it from falling into LO. The purpose of the convergent adaptive weighting strategy is to improve convergence behavior and global exploitation efficiency. Experiment result shows that it can effectively enhance the exploration efficiency of WOA. The paper introduces the two strategies to reconstruct a reinforced exploration mechanism whale optimization algorithm (REM-WOA). In order to verify its performance, 36 well known benchmark functions are selected for experiment from the CEC2017. Compared with other 12 meta-heuristic algorithms, the REM-WOA shows obvious advantages. Compared with 9 WOA and its variant algorithms, the REM-WOA has the best convergence behavior and strong global exploration efficiency. For three real design cases studies, REM-WOA has the best exploration efficiency.

Keywords: WOA; Global exploration; Continuous optimization; Convergence behavior (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:201:y:2022:i:c:p:23-48

DOI: 10.1016/j.matcom.2022.04.033

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