A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

被引:0
作者
Ruochen Liu
Yangyang Chen
Wenping Ma
Caihong Mu
Licheng Jiao
机构
[1] Xidian University,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China
来源
Soft Computing | 2014年 / 18卷
关键词
Coevolution; Dynamic multi-objective optimization; Evolutionary algorithms; Predictive model;
D O I
暂无
中图分类号
学科分类号
摘要
Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.
引用
收藏
页码:1913 / 1929
页数:16
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