Effects of Dominance Modification on Hypervolume-based and IGD-based Performance Evaluation Results of NSGA-II

Ishibuchi，Hisao; Pang，Lie Meng; Shang，Ke

10.1145/3583131.3590437

2023-07-15

Genetic and Evolutionary Computation Conference (GECCO)

GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference

679-687

JUL 15-19, 2023

null,Lisbon,PORTUGAL

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

In the field of evolutionary multi-objective optimization, it is well known that dominance-based algorithms do not work well on many-objective problems. This is because almost all solutions in a population become non-dominated in early generations. Two approaches have been proposed to decrease the number of non-dominated solutions. One is to increase the dominated region by each solution: dominance modification. The other is to increase the correlation among objectives: objective modification. In this paper, first we show that these two approaches can be viewed as the same approach. We also explain that some regions of the Pareto front are dominated when the dominated region is increased. Next, we numerically examine the effects of dominance modification on the performance of NSGA-II on many-objective test problems. Through computational experiments, we demonstrate that its positive and negative effects are clearly shown by the hypervolume (HV) and inverted generational distance (IGD) indicators, respectively. Then, we discuss why these two indicators emphasize different effects of dominance modification using the optimal distribution of solutions for each indicator. Finally, we explain that objective space normalization is needed in dominance modification whereas it has no effects on the Pareto dominance relation.

cone dominance evolutionary many-objective optimization evolutionary multi-objective optimization performance indicators

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China["62250710163","62250710682"]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:001031455100076

20233314553793

Multiobjective optimization

Optimization Techniques:921.5

2-s2.0-85167673150

Scopus

Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Ishibuchi，Hisao,Pang，Lie Meng,Shang，Ke. Effects of Dominance Modification on Hypervolume-based and IGD-based Performance Evaluation Results of NSGA-II[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2023:679-687.