Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization

被引:27
作者
Chen, Ying [1 ,2 ,3 ]
Zou, Juan [1 ,2 ,3 ]
Liu, Yuan [1 ,2 ,3 ]
Yang, Shengxiang [1 ,2 ,5 ]
Zheng, Jinhua [1 ,2 ,3 ,4 ]
Huang, Weixiong [1 ,2 ,3 ]
机构
[1] Xiangtan Univ, Key Lab Intelligent Comp & Informat Proc, Sch Comp Sci, Minist Educ, Xiangtan, Hunan, Peoples R China
[2] Xiangtan Univ, Sch Cyberspace Sci, Xiangtan, Hunan, Peoples R China
[3] Xiangtan Univ, Fac Sch Comp Sci, Xiangtan 411105, Peoples R China
[4] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China
[5] De Montfort Univ, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England
关键词
Dynamic multiobjective optimization problems; Evolutionary algorithms; Change response mechanism; GENETIC ALGORITHM; EVOLUTIONARY; INTELLIGENCE; DIVERSITY; TESTS;
D O I
10.1016/j.swevo.2022.101041
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The environments of the dynamic multiobjective optimization problems (DMOPs), such as Pareto optimal front (POF) or Pareto optimal set (POS), usually frequently change with the evolution process. This kind of problem poses a higher challenge for evolutionary algorithms because it requires the population to quickly track (i.e., con -verge) to the position of a new environment and be widely distributed in the search space. The prediction-based response mechanism is a commonly used method to deal with environmental changes, but it's only suitable for predictable changes. Moreover, the imbalance of population diversity and convergence in the process of tracking the dynamically changing POF has aggravated. In this paper, we proposed a new change response mechanism that combines a hybrid prediction strategy and a precision controllable mutation strategy (HPPCM) to solve the DMOPs. Specifically, the hybrid prediction strategy coordinates the center point-based prediction and the guiding individual-based prediction to make accurate predictions. Thus, the population can quickly adapt to the predictable environmental changes. Additionally, the precision controllable mutation strategy handles un-predictable environmental changes. It improves the diversity exploration of the population by controlling the variation degree of solutions. In this way, our change response mechanism can adapt to various environmental changes of DMOPs, such as predictable and unpredictable changes. This paper integrates the HPPCM mecha-nism into a prevalent regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) to optimize DMOPs. The results of comparative experiments with some state-of-the-art algorithms on various test instances have demonstrated the effectiveness and competitiveness of the change response mechanism proposed in this paper.
引用
收藏
页数:17
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