Use of Two Reference Points in Hypervolume-Based Evolutionary Multiobjective Optimization Algorithms

Ishibuchi, Hisao1; Imada, Ryo2; Masuyama, Naoki2; Nojima, Yusuke2

10.1007/978-3-319-99253-2_31

2018

16113349

1611-3349

PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XV, PT I

11101

384-396

Coimbra, Portugal

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Recently it was reported that the location of a reference point has a dominant effect on the optimal distribution of solutions for hypervolume maximization when multiobjective problems have inverted triangular Pareto fronts. This implies that the use of an appropriate reference point is indispensable when hypervolume-based EMO (evolutionary multiobjective optimization) algorithms are applied to such a problem. However, its appropriate reference point specification is difficult since it depends on various factors such as the shape of the Pareto front (e.g., triangular, inverted triangular), its curvature property (e.g., linear, convex, concave), the population size, and the number of objectives. To avoid this difficulty, we propose an idea of using two reference points: one is the nadir point, and the other is a point far away from the Pareto front. In this paper, first we demonstrate that the effect of the reference point is strongly problem-dependent. Next we propose an idea of using two reference points and its simple implementation. Then we examine the effectiveness of the proposed idea by comparing two hypervolume-based EMO algorithms: one with a single reference point and the other with two reference points.

Evolutionary multiobjective optimization (EMO) Hypervolume-based algorithms Reference point specification Hypervolume contribution

第一 ; 通讯

英语

CPCI-S ; EI

Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic

WOS:000458557700031

20183905853381

Evolutionary algorithms ; Multiobjective optimization ; Population statistics ; Specifications

Codes and Standards:902.2 ; Optimization Techniques:921.5

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China
2.Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Naka Ku, 1-1 Gakuen Cho, Sakai, Osaka 5998531, Japan

Ishibuchi, Hisao,Imada, Ryo,Masuyama, Naoki,et al. Use of Two Reference Points in Hypervolume-Based Evolutionary Multiobjective Optimization Algorithms[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2018:384-396.