A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems

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.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

The Whale optimization algorithm (WOA) suffers from weak exploration ability, poor convergence behavior, and a tendency to fall into local optima. To address these issues, this study analyzes WOA from the perspectives of global exploration efficiency and convergence characteristics, and proposes population redistribution and convergent adaptive weighting strategies. Experimental results demonstrate that these strategies effectively enhance the exploration efficiency and convergence behavior of WOA. The Reinforced Exploration Mechanism Whale Optimization Algorithm (REM-WOA) shows advantages over other meta-heuristic algorithms and achieves the best exploration efficiency in three real design case studies.