An adaptive multiobjective evolutionary algorithm based on grid subspaces

被引：8
|
作者：
Li, Linlin [1 ]
Wang, Xianpeng [2 ]
机构：
[1] Northeastern Univ, Key Lab Data Analyt & Optimizat Smart Ind, Minist Educ, Shenyang 110819, Peoples R China
[2] Liaoning Key Lab Mfg Syst & Logist, Liaoning Engn Lab Operat Analyt & Optimizat Smart, Shenyang 110819, Peoples R China
来源：
MEMETIC COMPUTING | 2021年 / 13卷 / 02期
基金：
中国国家自然科学基金; 国家自然科学基金重大项目;
关键词：
Grid; External archive; Multi-objective evolutionary algorithm; Dominance relationship; OPTIMIZATION;
D O I：
10.1007/s12293-021-00336-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds of multiobjective problems have attracted considerable attention in recent years. In this paper, an adaptive multi-objective evolutionary algorithm is proposed by incorporating the concepts of the grid system (denoted as AGMOEA). Based on grid, the objective space is divided into subspaces. Based on the quality and dominance relationship between subspaces, the evolutionary opportunities are dynamically allocated to different subspaces with an adaptive selection strategy. To improve the evolutionary efficiency, the evolutionary scheme and an external archive mechanism considering representative individuals are proposed. The experimental results on 21 benchmark problems demonstrate that the proposed algorithm is competitive or superior to the rival algorithms.
引用
收藏
页码：249 / 269
页数：21
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