Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multi-objective Optimization Algorithms

Chen，Weiyu; Ishibuchi，Hisao; Shang，Ke

10.1109/SSCI44817.2019.9002906

2019-12-01

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

1826-1833

6-9 Dec. 2019

Xiamen, China

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

Institute of Electrical and Electronics Engineers Inc.

Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the performance of evolutionary multi-objective optimization (EMO) algorithms on continuous multi-objective test problems. In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems. However, the effect of the simultaneous discretization of both spaces has not been examined in the literature. In this paper, we examine the effects of the decision space discretization, objective space discretization and simultaneous discretization on the performance of NSGA-II through computational experiments on the DTLZ and WFG problems. Using various settings about the number of decision variables and the number of objectives, our experiments are performed on four types of problems: standard problems, large-scale problems, many-objective problems, and large-scale many-objective problems. We show that the decision space discretization has a positive effect for large-scale problems and the objective space discretization has a positive effect for many-objective problems. We also show the discretization of both spaces is useful for large-scale many-objective problems.

decision space discretization evolutionary multi-objective optimization large-scale problems many-objective problems multi-objective problems objective space discretization

第一

英语

EI ; CPCI-S

Guangdong Innovative and Entrepreneurial Research Team Program[2017ZT07X386] ; [2017KSYS008] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; [ZDSYS201703031748284] ; National Natural Science Foundation of China[61876075]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467201134

20201108276763

Artificial intelligence ; Evolutionary algorithms

Artificial Intelligence:723.4 ; Optimization Techniques:921.5

2-s2.0-85080913646

Scopus

Southern University of Science and Technology (SUSTech),Shenzhen Key Laboratory of Computational Intelligence,University Key Laboratory of Evolving,Intelligent Systems of Guangdong Province,Shenzhen,China

Chen，Weiyu,Ishibuchi，Hisao,Shang，Ke. Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multi-objective Optimization Algorithms[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:1826-1833.