Use of Inverted Triangular Weight Vectors in Decomposition-Based Multiobjective Algorithms

Ishibuchi, Hisao1,2; Imada, Ryo2; Doi, Ken2; Nojima, Yusuke2

10.1109/SMC.2017.8122632

2017

1062-922X

978-1-5386-1646-8

2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)

2017-January

373-378

5-8 Oct. 2017

Banff, AB, Canada

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Recently a number of evolutionary multiobjective optimization algorithms have been proposed in the framework of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition). A multiobjective problem is decomposed into multiple single-objective problems using a set of weight vectors in MOEA/D. The number of single-objective problems is the same as the number of weight vectors, which is also the same as the population size. It is well known that the performance of MOEA/D depends on the shape of the Pareto front. Weight vectors in MOEA/D are specified by using a triangular simplex lattice structure. Thus MOEA/D works well on multiobjective problems with triangular Pareto fronts, and has difficulties in handling inverted triangular Pareto fronts. One may wonder what happens if an inverted triangular simplex lattice structure is used for generating weight vectors in MOEA/D. This is our research question. In this paper, first we explain how to use an inverted triangular simplex lattice structure for generating weight vectors. Next we analytically explain inherent difficulties in the use of inverted triangular weight vectors in MOEA/D. Then, through computational experiments, we examine the performance of two types of MOEA/D: One is with triangular weight vectors, and the other is with inverted triangular weight vectors. Our discussions show that the use of inverted triangular weight vectors is not a good choice except for some special cases.

Evolutionary multiobjective optimization MOEA/D weight vector specification simplex lattice inverted triangular Pareto fronts

第一 ; 通讯

英语

CPCI-S ; EI

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Cybernetics

WOS:000427598700065

20181304957808

Cybernetics ; Multiobjective optimization ; Population statistics ; Vectors

Algebra:921.1 ; Optimization Techniques:921.5

Web of Science

1.Southern Univ Sci & Technol SUSTech, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China
2.Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Sakai, Osaka 5998531, Japan

Ishibuchi, Hisao,Imada, Ryo,Doi, Ken,et al. Use of Inverted Triangular Weight Vectors in Decomposition-Based Multiobjective Algorithms[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:373-378.