.png){kind=icon}
返回
A repulsive-distance-based maximum diversity selection algorithm for multimodal multiobjective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.112516.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multimodal multiobjective optimization problems (MMOPs) area challenging class of problems. Several advanced evolutionary algorithms have been developed to solve MMOPs, but these algorithms still have some limitations, such as having many parameters, slow convergence speed, and unsatisfactory performance. To solve these problems, we propose a repulsive-distance-based maximum diversity selection algorithm (RMDS) which aims, during environmental selection, to select individuals with the best comprehensive diversity through the repulsive distance. The repulsive distance is the comprehensive Euclidean distance between an individual and selected individuals with the consideration of distribution of individuals in both the decision and objective spaces. RMDS has the following advantages: first, the repulsive distance allows rational selection of well-distributed solutions in the non-parameterized case. Second, the repulsive distance acts both in the decision and objective spaces, so it provides a good balance between the distribution of the solution set in these two spaces. Third, RDMS has a straightforward principle. Experimental results show that RMDS has superior performance incomparison with other well-known multimodal multiobjective evolutionary algorithms on 31 test functions.
Keyword:
Multimodel multiobjective optimization problems
Multimodel multiobjective evolutionary algorithms
Replusive distance
Distribution
Convergence
期刊
IF:
6.6
论文数:
1.4W
被引数:
5.9W
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A two-stage preference driven multi-objective evolutionary algorithm for workflow scheduling in the Cloud基于两阶段偏好驱动的云工作流调度多目标进化算法
PREAI
分享收藏分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容