TY - JOUR
T1 - An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems
AU - Shen, Ya
AU - Zhang, Chen
AU - Soleimanian Gharehchopogh, Farhad
AU - Mirjalili, Seyedali
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 61806068, in part by the Universities Natural Science Research Project of Anhui Provincial under Grant KJ2021ZD0118 and in part by Anhui Provincial University Outstanding Talent Cultivation Project under Grant gxgnfx2020117. It was also supported by Hefei University Graduate Innovation and Entrepreneurship Project under Grant 21YCXL15.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/4/1
Y1 - 2023/4/1
N2 - 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 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.
AB - 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 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.
KW - Algorithm
KW - Benchmark
KW - Engineering design optimization
KW - Global optimization
KW - Metaheuristic
KW - Multi-population
KW - Whale optimization algorithm
KW - WOA
UR - http://www.scopus.com/inward/record.url?scp=85145256818&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.119269
DO - 10.1016/j.eswa.2022.119269
M3 - Article
AN - SCOPUS:85145256818
SN - 0957-4174
VL - 215
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 119269
ER -