Differential evolution guided by approximated Pareto set for multiobjective optimization

Wang，Shuai1; Zhou，Aimin1; Li，Bingdong1; Yang，Peng2

2023-06-01

10.1016/j.ins.2023.02.043

INFORMATION SCIENCES   Impact Factor & Quartile

0020-0255

1872-6291

Differential evolution (DE), as an efficient evolutionary optimizer, has been widely applied to deal with multiobjective optimization problems. In DE generation operations, appropriate guiding solutions, the “best” solutions (denoted as x), will be in favor of the search for generating promising new trial solutions. However, it is still a challenge to define and select such x due to the Pareto property of multiobjective optimization. Facing this challenge, we propose a regularity model guided differential evolution (RMDE) for multiobjective optimization. Different from the existing studies that select x from non-dominated solutions or predefined preference solutions, the proposed RMDE aims to sample the guiding solutions from the regularity models that are built to approximate Pareto optimal set explicitly. In this way, four alternative RMDE mutation strategies with the sampled x are developed and investigated, including the search efficiency and parameter settings. Empirical studies are conducted to validate the performance of RMDE on 51 test instances. The experimental results demonstrate the advantages of the proposed method over seven other classical or newly developed algorithms from the literature.

Differential evolution Evolutionary algorithm Guiding solution Multiobjective optimization Regularity model

SCI

English

Others

Scientific and Technological Innovation 2030 Major Projects[2018AAA0100902] ; Science and Technology Commission of Shanghai Municipality[19511120601]

Computer Science

Computer Science, Information Systems

WOS:000949422000001

ELSEVIER SCIENCE INC

COMPUTER SCIENCE

2-s2.0-85149058864

Scopus

1.Shanghai Institute of AI for Education,School of Computer Science and Technology,East China Normal University,Shanghai,200062,China
2.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Wang，Shuai,Zhou，Aimin,Li，Bingdong,et al. Differential evolution guided by approximated Pareto set for multiobjective optimization[J]. INFORMATION SCIENCES,2023,630:669-687.

Wang，Shuai,Zhou，Aimin,Li，Bingdong,&Yang，Peng.(2023).Differential evolution guided by approximated Pareto set for multiobjective optimization.INFORMATION SCIENCES,630,669-687.

Wang，Shuai,et al."Differential evolution guided by approximated Pareto set for multiobjective optimization".INFORMATION SCIENCES 630(2023):669-687.