A new multi-objective optimization algorithm combined with opposition-based learning

作者：

Highlights：

• A new multi-objective optimization method used OBL strategy, WOA and DE algorithms

• It combines DE and the OBL to improve the performance of the WOA

• The MWDEO results outperformed all other algorithms in most of the test problems

• 32 multi-objective test problems are used in the experiments and CEC2017 problems

摘要

•A new multi-objective optimization method used OBL strategy, WOA and DE algorithms•It combines DE and the OBL to improve the performance of the WOA•The MWDEO results outperformed all other algorithms in most of the test problems•32 multi-objective test problems are used in the experiments and CEC2017 problems

论文关键词：Multi-objective optimization,Whale optimization algorithm,Differential evolution,Opposition-based learning

论文评审过程：Received 31 March 2020, Revised 14 July 2020, Accepted 3 August 2020, Available online 1 September 2020, Version of Record 24 September 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113844