Dynamic multiobjective evolutionary algorithm with adaptive response mechanism selection strategy
作者：
Highlights：
• This paper proposed an adaptive response mechanism selection (ARMS) framework and a dynamic MOEA.
• The ARMS framework can adaptively select the most effective response mechanisms based on their recent performance.
• An overall evaluation strategy that assigned rewards to the response mechanism was adopted.
• The simple ARMS framework can easily incorporate more response mechanisms.
• The statistical results clearly demonstrated that MOEA/D-ARMS were superior to the compared algorithms.
摘要
•This paper proposed an adaptive response mechanism selection (ARMS) framework and a dynamic MOEA.•The ARMS framework can adaptively select the most effective response mechanisms based on their recent performance.•An overall evaluation strategy that assigned rewards to the response mechanism was adopted.•The simple ARMS framework can easily incorporate more response mechanisms.•The statistical results clearly demonstrated that MOEA/D-ARMS were superior to the compared algorithms.
论文关键词：Dynamic multiobjective optimization,Adaptive response mechanism selection,Evolutionary algorithm,Response mechanism
论文评审过程：Received 3 July 2021, Revised 1 November 2021, Accepted 25 March 2022, Available online 6 April 2022, Version of Record 23 April 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.108691
