Many-Objective Evolutionary Algorithm Based On Decomposition With Random And Adaptive Weights

被引:0
作者
Farias, Lucas R. C. [1 ]
Araujo, Aluizio F. R. [1 ]
机构
[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil
来源
2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC) | 2019年
关键词
PERFORMANCE; MOEA/D; OPTIMIZATION;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Decomposition-based evolutionary algorithms that work with an appropriate set of weights might obtain a quality final solution set in spite of the use of uniformly distributed and fixed weights that has two important limitations: it may fail depending on the problem geometry; and the population size is not flexible when dealing with Many-objective Problems (MaOPs). Recently proposed, the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/D-URAW) deals with these limitations using uniformly randomly weights generation method and weight adaptation based on the population sparsity. This paper validates this new approach, the MOEA/D-URAW, with state-of-the-art evolutionary algorithms in MaOPs, i.e., WFG1-WFG9 and MOKP with 5, 10 and 15 objectives. The results suggest the effectiveness of this approach.
引用
收藏
页码:3746 / 3751
页数:6
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