.png){kind=icon}
返回
A diversity ranking based evolutionary algorithm for multi-objective and many-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2019.03.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, a variety of multi-objective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, the performance of most existing MOEAs is sensitive to the Pareto front (PF) shapes of the problem to be solved, and it is difficult for these algorithms to manage diversity on various types of Pareto fronts (PFs) effectively. To address these issues, this paper proposes an evolutionary algorithm based on diversity ranking method for multi-objective and many-objective optimization. The proposed evolutionary algorithm introduces reference vector adaptation method to solve different shapes of Pareto fronts, and proposes the diversity ranking method to manage diversity. The extensive experimental results demonstrate that the proposed algorithm can solve various types of Pareto fronts, surpassing several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization.
Keyword:
Evolutionary algorithm
Multi-objective optimization
Many-objective optimization
Reference vector adaptation
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:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
Preparation of Porous PVDF Nanofiber from PVDF/PVP Blend by Electrospray Deposition电喷雾沉积法制备PVDF/PVP共混物多孔PVDF纳米纤维
OAAI
.png){kind=icon}
OAAI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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization在进化多目标优化中使用平均Hausdorff距离作为性能度量
PREAI
分享收藏分享收藏分享收藏
查看更多内容