.png){kind=icon}
返回
A multimodal multi-objective evolutionary algorithm with two-stage dual-indicator selection strategy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2023.101319.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multimodal multi-objective problems (MMOPs) arise frequently in the real world, in which multiple Pareto optimal solution sets correspond to the same point on the Pareto front. To find more equivalent Pareto sets, numerous multimodal multi-objective evolutionary algorithms (MMEAs) based on niching have been developed. However, traditional niching methods have parameter sensitivity issues. Moreover, most existing MMEAs always perform diversity maintenance operations in the decision space and the objective space sequentially based on preferences, without considering a diversity maintenance operation that takes into account both the decision space and the objective space simultaneously. To tackle these challenges, a multimodal multi-objective evolutionary algorithm with two-stage double indicator selection strategy (MMEA-TDI) is developed. First, a dual-indicator with adaptive niche radius is developed to estimate the crowding status of the population. Then, a diversity-based mating selection is suggested to select well-distributed parents for mating. Furthermore, the environmental selection is performed in two stages. In the first stage, a parameter -free automatic niching technique based on clustering is adopted to well balance diversity and convergence in the decision space, while a double indicator selection strategy is performed for maintaining diversity in both decision and objective spaces in the second stage. To assess the performance of the proposed algorithm, extensive experiments are conducted on the well-known benchmark functions, in comparison with eight state -of-the-art MMEAs. Experimental results demonstrate that the proposed algorithm is significantly superior to the competing algorithms.
Keyword:
Multimodal multi-objective
Evolutionary algorithm
Clustering
Diversity indicator
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems基于双存档模型的多峰多目标优化进化算法
.png){kind=icon}
PREAI
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