计算机应用研究2017，Vol.34Issue(12)：3580-3584,5.DOI:10.3969/j.issn.1001-3695.2017.12.014
基于距离收敛量和历史信息密度的多目标进化算法
Distance convergence and history density based multi-objective evolutionary algorithm
李潇涵 1刘博 1张友1
作者信息
- 1. 东北师范大学计算机科学与信息技术学院,长春130024
- 折叠
摘要
Abstract
Most multi-objective evolutionary algorithms used Pareto dominant criterion and density criterion to select individuals from generation to generation.However,as the Pareto criterions failed to discriminate the convergence and diversity degrees of individuals when the individuals were Pareto-optimal solutions.To address this issue,this paper proposed a distance convergence and history density based multi-objective evolutionary algorithm.It defined the distance convergence criterion to distinguish Pareto-optimal individuals,and presented the history density criterion to obtain a more accurate density estimation.The experimental results show that this algorithm performs competitively with respect to chosen state-of-the-art designs.关键词
多目标进化算法/距离收敛量/历史信息密度/配对选择Key words
multi-objective evolutionary algorithm/distance convergence/history density/mating selection分类
信息技术与安全科学引用本文复制引用
李潇涵,刘博,张友..基于距离收敛量和历史信息密度的多目标进化算法[J].计算机应用研究,2017,34(12):3580-3584,5.
