A Parameterless Decomposition-based Evolutionary Multi-objective Algorithm

被引：0

作者：

Gu, Fangqing ^{[1
]}

Cheung, Yiu-ming ^{[2
]}

Liu, Hai-Lin ^{[1
]}

Lin, Zixian ^{[1
]}

机构：

[1] Guangdong Univ Technol, Guangzhou, Guangdong, Peoples R China

[2] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China

来源：

PROCEEDINGS OF 2018 TENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2018年

基金：

中国国家自然科学基金;

关键词：

Evolutionary computation; multi-objective optimization; parameterless decomposition; uniformity solution; NONDOMINATED SORTING APPROACH; NUMBER; MOEA/D;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Multiobjective evolutionary algorithm based on decomposition has made a great contribution to the field of evolutionary multiobjective optimization problem. The decomposition based algorithms construct a number of scalar optimization subproblems by using a set of weight vectors, and optimize these subproblems simultaneously to approximate the Pareto front (PF). The weight vectors have a massive influence on the performance of the decomposition-based algorithm, especially for the multiobjective optimization problems (MOP) with a complex PF. To solve this, we propose a parameterless decomposition scheme to adjust the weight vectors automatically. Experiment results indicate that the proposed algorithm can obtain better uniformity solutions for the MOP with complex PF.

引用

页码：842 / 845

页数：4

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