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

• The population is divided into three sub-populations. • A novel population evolution strategy is introduced to improve the basic WOA. • The proposed algorithm (MEWOA) has high solution accuracy and converges fast. • CEC 2019 test suite and 30 benchmark functions are used to test MEWOA. • MEWOA is applied to four engineering design problems. The whale optimization algorithm (WOA) tends to suffer from slow convergence speed and quickly falling into the local optimum. To alleviate these drawbacks, a WOA variant is proposed based on multi-population evolution (MEWOA) to better solve 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 dimensions 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 results demonstrate the competitiveness and merits of the proposed MEWOA algorithm.