.png){kind=icon}
返回
A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2019.2949841.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multimodal multiobjective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multiobjective optimization, they are likely to perform poorly for multimodal multiobjective optimization due to the lack of mechanisms to maintain the solution space diversity. To address this issue, this article proposes a framework to improve the performance of decomposition-based evolutionary algorithms for multimodal multiobjective optimization. Our framework is based on three operations: 1) assignment; 2) deletion; and 3) addition operations. One or more individuals can be assigned to the same subproblem to handle multiple equivalent solutions. In each iteration, a child is assigned to a subproblem based on its objective vector, i.e., its location in the objective space. The child is compared with its neighbors in the solution space assigned to the same subproblem. The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets. Results show that the improved versions perform clearly better than their original algorithms.
Keyword:
Pareto optimization
Indexes
Evolutionary computation
Spatial diversity
Decision making
Benchmark testing
Decomposition-based evolutionary algorithms
multimodal multiobjective optimization
reference vector-based evolutionary algorithms
solution space diversity
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12
论文数:
1.8K
被引数:
2.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications寻求多种解决方案: 关于小众方法及其应用的最新调查OAAI
OAAI分享收藏
查看更多内容