.png){kind=icon}
返回
A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-013-1175-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Coevolution
Dynamic multi-objective optimization
Evolutionary algorithms
Predictive model
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
分享收藏
Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏
查看更多内容