Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization

Ishibuchi，Hisao1; Fukase，Takefumi2; Masuyama，Naoki2; Nojima，Yusuke2

10.1145/3205455.3205644

2018-07-02

GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference

665-672

Kyoto, Japan

Association for Computing Machinery, Inc

A promising idea for evolutionary constrained optimization is to efficiently utilize not only feasible solutions (feasible individuals) but also infeasible ones. In this paper, we propose a simple implementation of this idea in MOEA/D. In the proposed method, MOEA/D has two grids of weight vectors. One is used for maintaining the main population as in the standard MOEA/D. In the main population, feasible solutions always have higher fitness than infeasible ones. Among infeasible solutions, solutions with smaller constraint violations have higher fitness. The other grid is for maintaining a secondary population where non-dominated solutions with respect to scalarizing function values and constraint violations are stored. More specifically, a single non-dominated solution with respect to the scalarizing function and the total constraint violation is stored for each weight vector. A new solution is generated from a pair of neighboring solutions in the two grids. That is, there exist three possible combinations of two parents: both from the main population, both from the secondary population, and each from each population. The proposed MOEA/D variant is compared with the standard MOEA/D and other evolutionary algorithms for constrained multiobjective optimization through computational experiments.

Constrained multiobjective optimization Decomposition-based multiobjective optimization algorithms Evolutionary multiobjective optimization (EMO) MOEA/D

第一

英语

EI ; CPCI-S

Innovation and Technology Commission[ZDSYS201703031748284]

20183105630520

Constrained optimization ; Evolutionary algorithms

Optimization Techniques:921.5 ; Systems Science:961

2-s2.0-85050607708

Scopus

1.Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, ,Shenzhen,518055,China
2.Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, ,Sakai, Osaka,599-8531,Japan

Ishibuchi，Hisao,Fukase，Takefumi,Masuyama，Naoki,et al. Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization[C]:Association for Computing Machinery, Inc,2018:665-672.