Liu Ruochen, Li Jianxia, Jin Yaochu, Jiao Licheng
Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, 710071, China
Department of Computer Science, University of Surrey, Guildford, GU2 7XH, United Kingdom
Evol Comput. 2021 Dec 1;29(4):491-519. doi: 10.1162/evco_a_00289.
Dynamic multiobjective optimization deals with simultaneous optimization of multiple conflicting objectives that change over time. Several response strategies for dynamic optimization have been proposed, which do not work well for all types of environmental changes. In this article, we propose a new dynamic multiobjective evolutionary algorithm based on objective space decomposition, in which the maxi-min fitness function is adopted for selection and a self-adaptive response strategy integrating a number of different response strategies is designed to handle unknown environmental changes. The self-adaptive response strategy can adaptively select one of the strategies according to their contributions to the tracking performance in the previous environments. Experimental results indicate that the proposed algorithm is competitive and promising for solving different DMOPs in the presence of unknown environmental changes. Meanwhile, the proposed algorithm is applied to solve the parameter tuning problem of a proportional integral derivative (PID) controller of a dynamic system, obtaining better control effect.
动态多目标优化处理多个随时间变化的相互冲突目标的同时优化。已经提出了几种用于动态优化的响应策略，但并非对所有类型的环境变化都有效。在本文中，我们提出了一种基于目标空间分解的新型动态多目标进化算法，其中采用最大最小适应度函数进行选择，并设计了一种集成多种不同响应策略的自适应响应策略来处理未知的环境变化。自适应响应策略可以根据它们在先前环境中对跟踪性能的贡献自适应地选择其中一种策略。实验结果表明，所提出的算法在存在未知环境变化的情况下，对于解决不同的动态多目标优化问题具有竞争力且前景广阔。同时，将所提出的算法应用于解决动态系统比例积分微分（PID）控制器的参数调整问题，获得了更好的控制效果。
