Proposal of a New Test Problem for Large-Scale Multi- and Many-Objective Optimization

Pang，Lie Meng; Shang，Ke; Chen，Longcan; Ishibuchi，Hisao; Chen，Weiyu

10.1109/SMC52423.2021.9659260

2021

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

1062-922X

978-1-6654-4208-4

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

484-491

OCT 17-20, 2021

null,null,ELECTR NETWORK

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

IEEE

The research on large-scale multi- and many-objective optimization has received increasing attention in the evolutionary multi-objective optimization (EMO) community. A number of large-scale EMO algorithms based on different strategies (e.g., divide-and-conquer, coevolution, and dimensionality reduction) have been proposed over the last decade. The performance of the large-scale EMO algorithms was empirically evaluated using several benchmark test suites, including the ZDT, DTLZ, WFG, MaF, UF and LSMOP test suites. Even though these test suites are theoretically scalable to any number of decision variables, they are not necessarily appropriate for examining the performance of large-scale EMO algorithms. In fact, among these benchmark test suites, only the LSMOP test suite is specifically designed to test the performance of large-scale EMO algorithms. In this paper, we propose a new scalable multi- and many-objective test problem for examining large-scale EMO algorithms. The proposed test problem has the following features: 1) the number of objectives and decision variables can be arbitrarily specified; 2) the interaction strength among the objectives can be adjusted by a correlation parameter. The performance of six EMO algorithms is examined on the new test problem. Our experimental results show that the proposed new test problem poses difficulties to some state-of-the-art large-scale EMO algorithms.

benchmark test problems Evolutionary many-objective optimization large-scale many-objective optimization

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[61876075];National Natural Science Foundation of China[62002152];

Computer Science

Computer Science, Cybernetics ; Computer Science, Information Systems

WOS:000800532000072

20220711616797

Benchmarking ; Evolutionary algorithms ; Multiobjective optimization ; Testing

Management:912.2 ; Optimization Techniques:921.5

2-s2.0-85124307552

Scopus

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

Pang，Lie Meng,Shang，Ke,Chen，Longcan,et al. Proposal of a New Test Problem for Large-Scale Multi- and Many-Objective Optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:484-491.