An Improved MOEA/D Utilizing Variation Angles for Multi-Objective Optimization

被引：1
|
作者：
Sato, Hiroyuki [1 ]
Miyakawa, Minami [2 ]
Takadama, Keiki [1 ]
机构：
[1] Univ Electrocommun, 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japan
[2] Hosei Univ, 3-7-2 Kajino Cho, Koganei, Tokyo 1848584, Japan
来源：
PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCO'17 COMPANION) | 2017年
关键词：
Multi-objective optimization; MOEA/D;
D O I：
10.1145/3067695.3076037
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work proposes a decomposition-based multi-objective evolutionary algorithm utilizing variation angles among objective and weight vectors. The proposed algorithm introduces an angle-based proportional selection and dominance- and angle-based solution comparison criterion. Experimental results using WFG4 and WFG5 problems show that the proposed algorithm achieves better search performance than the conventional MOEA/D and MOEA/D-CRU.
引用
收藏
页码：163 / 164
页数：2
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