A two-stage hypervolume contribution approximation method based on R2 indicator

Y. Nan; K. Shang; H. Ishibuchi; L. He

10.1109/CEC45853.2021.9504726

2021

Proc. of 2021 IEEE Congress on Evolutionary Computation

978-1-7281-8394-7

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)

2468-2475

June 28 - July 1, 2021

Kraków, Poland

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

IEEE

Hypervolume-based multi-objective evolutionary algorithms (HV-MOEAs) are one of the popular algorithm classes in the evolutionary multi-objective optimization (EMO) community. HV-MOEAs, which can directly optimize the HV of a solution set, are useful in various applications. However, the computation time of HV-MOEAs is very long for many-objective problems since the calculation of the hypervolume contribution (HVC) is computationally expensive. Therefore, a number of approximation methods for the HVC calculation were proposed to reduce its time cost. An R2-based hypervolume contribution approximation (R2-HVC) method was proposed for HVC approximation. However, for HV-MOEAs, the point is to find the worst solution, instead of accurately approximating the HVC of each solution. In this paper, a novel method (i.e., two-stage R2-HVC) is proposed for improving the ability of R2-HVC to correctly identify the worst solution (i.e., the solution with the smallest HVC value) in a solution set. In the proposed method, some candidate solutions are selected based on rough HVC approximation in the first stage, and they are carefully evaluated in the second stage. It is shown through computational experiments that the proposed method performs much better than the original R2-HVC method.

Multi-objective optimization indicator-based algorithms hypervolume hypervolume contribution

第一

英语

CPCI-S ; EI

National Natural Science Foundation of China[62002152,61876075]

Computer Science ; Engineering ; Mathematical & Computational Biology ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Mathematical & Computational Biology ; Operations Research & Management Science

WOS:000703866100311

20220711650729

Approximation algorithms ; Approximation theory ; Evolutionary algorithms

Mathematics:921 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

人工提交

Southern University of Science and Technology

Y. Nan,K. Shang,H. Ishibuchi,et al. A two-stage hypervolume contribution approximation method based on R2 indicator[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:2468-2475.