.png){kind=icon}
返回
Improved differential evolution using two-stage mutation strategy for multimodal multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2023.101232.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, multimodal multi-objective problem (MMOP) has become a popular research field in multi-objective optimization problems. Multimodal multi-objective optimization problem has multiple equivalent Pareto sets corresponding to one same Pareto front, and the goal of solving it is to find all the equivalent Pareto sets, which is divergent from multi-objective optimization problem. To resolve this problem, an improved differential evolu-tion for multimodal multi-objective optimization is proposed. First and foremost, a novel distance indicator, called modified maximum extension distance (MMED), is proposed. Secondly, a two-stage mutation strategy and a novel mutation strategy, namely DE/rand-to-MMEDBest/2, are designed to boost the diversity and convergence in different evolution stages of population. Thirdly, a MMED-based environmental selection that takes in-dividuals in lesser fronts into population is used to enhance the overall performance of the population. Ulti-mately, the proposed algorithm is compared to multiple selected outstanding algorithms in solving multiple multimodal multi-objective optimization problems. Experimental results indicate that the proposed algorithm is capable of solving multimodal multi-objective optimization problems effectively and has achieved excellent performance.
Keyword:
Multimodal multi-objective optimization prob-lem
Differential evolution
Two-stage mutation
Modified maximum extension distance
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
分享收藏Multimodal Multiobjective Evolutionary Optimization With Dual Clustering in Decision and Objective Spaces决策和目标空间中具有双重聚类的多模态多目标进化优化
PREAI
分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏Automatic Niching Differential Evolution With Contour Prediction Approach for Multimodal Optimization Problems多模态优化问题的自动Niching差分进化与轮廓预测方法
OAAI
OAAI分享收藏A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems基于自组织形态的多目标粒子群算法求解多模态多目标问题
PREAI
分享收藏
查看更多内容