The Effect of Feasible Region on Imbalanced Problem in Constrained Multi-objective Optimization

被引：1

作者：

Lin, Jiabin ^{[1
]}

Liu, Hai-Lin ^{[1
]}

Peng, Chaoda ^{[1
]}

机构：

[1] Guangdong Univ Technol, Fac Appl Math, Guangzhou, Guangdong, Peoples R China

来源：

2017 13TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2017年

关键词：

Evolutionary Algorithm; Constrained Multiobjective Optimization; Imbalanced Problem; Decomposition; ALGORITHM;

D O I：

10.1109/CIS.2017.00026

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The decomposition-based multi-objective evolutionary algorithm, i.e. MOEA/D-M2M, has shown to be an efficient algorithm to solve unconstrained imbalanced multi-objective optimization problems. However, the use in constrained imbalanced multi-objective optimization problems has not been fully explored. In this paper, we study the factors that impact the constrained imbalanced multi-objective optimization problems. To begin with, a series of constrained imbalanced multi-objective optimization problems are constructed. Then three kinds of representative algorithms, i.e. NSGA-II, MOEA/D and MOEA/D-M2M, combined with the constraint domination principle respectively, are utilized to solve them. The experimental results demonstrate that MOEA/D-M2M works better than the other two compared algorithms on constrained imbalanced multi-objective optimization problems in terms of the reliability and stability of finding a set of well distributed non-domination solutions.

引用

页码：82 / 86

页数：5

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