An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems

The whale optimization algorithm (WOA) tends to suffer from slow convergence speed and quickly falling into the local optimum. In this work, a WOA variant is proposed based on multi-population evolution (MEWOA) to address these problems. Firstly, individuals are classified into three equal-sized sub-populations: exploratory sub -population, exploitative sub-population, and modest sub-population, according to their fitness. Secondly, the moving strategies of each sub-population are assigned using different mechanisms. The exploratory and exploitative sub-populations perform global and local search, respectively, while the modest sub-population randomly explores or exploits the search space. Finally, we introduce a novel population evolution strategy to help MEWOA improve its global optimization ability and avoid local optimum. MEWOA is compared with five state-of-the-art WOA variants and seven basic metaheuristic algorithms over 30 benchmark functions with di-mensions of 100, 500, 1000, and 2000 respectively. It is observed that MEWOA achieves faster convergence speed, shows shorter runtime, and provides higher solution accuracy than other algorithms on the majority of benchmark functions. In addition, we tested MEWOA's ability to solve challenging real-world and constrained optimization problems on the CEC 2019 test suite and four engineering design problems. The experimental re-sults demonstrate the competitiveness and merits of the proposed MEWOA algorithm.

Automated summary

In this work, a multi-population evolution based variant of the whale optimization algorithm (MEWOA) is proposed to solve the slow convergence and local optimum problems. MEWOA divides individuals into three sub-populations and assigns different moving strategies to each sub-population, performing global and local search. The introduction of a novel population evolution strategy further enhances MEWOA's global optimization ability. Experimental results demonstrate the competitiveness and merits of MEWOA, achieving faster convergence speed, shorter runtime, and higher solution accuracy compared to other algorithms on benchmark functions and real-world problems.