.png){kind=icon}
返回
A robust performance evaluation approach for solution preservation in multiobjective optimization
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-022-00889-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many real-world optimization problems have complex features, such as bias, multimodel, etc. Existing evolutionary algorithms mainly utilize solutions' current performance to decide their survivals, which are not comprehensive enough to describe the evolving trend, and may misguide the evolve decision. In this paper, a novel robust performance evaluation approach for evolutionary multiobjective optimization algorithm is proposed. Here, the robustness refers to the performance fluctuation degree among several generations, which can be expressed by interval values in respect to the decision and objective spaces. Based on the robust performance evaluation, solutions can be selected and preserved considering their historical performance, and thus, the exploration strength in convergence potential areas can be maintained. Meanwhile, to construct an evolutionary algorithm that embeds robustness evaluation, a robust elite managerial method and a learning-based updating strategy are also designed. Experiments on multiobjective benchmark problems and a real-world optimization in a robotic manipulation system have proved the superiority of the proposed approach.
Keyword:
Multiobjective
Evolutionary algorithm
Elite preservation
Robustness
Historical information
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
.png){kind=icon}
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏Multipopulation Ant Colony System With Knowledge-Based Local Searches for Multiobjective Supply Chain Configuration基于知识局部搜索的多种群蚁群多目标供应链配置系统
PREAI
分享收藏
查看更多内容