A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization

Ming，Mengjun1; Wang，Rui2; Ishibuchi，Hisao3; Zhang，Tao2

2021

10.1109/TEVC.2021.3131124

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION   影响因子和分区

1089-778X

1941-0026

In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multi-objective optimization problems (CMOPs). However, most of the existing constrained multi-objective evolutionary algorithms (CMOEAs) cannot effectively explore and exploit those solutions, and therefore exhibit poor performance when facing problems with large infeasible regions. To address the issue, this paper proposes a novel method called DD-CMOEA, which features dual stages (i.e., exploration and exploitation) and dual populations. Specifically, the two populations, called mainPop and auxPop, first individually evolve with and without considering the constraints, responsible for exploring feasible and infeasible solutions, respectively. Then, in the exploitation stage, mainPop provides information about the location of feasible regions, which facilitates auxPop to find and exploit surrounding infeasible solutions. The promising infeasible solutions obtained by auxPop in turn help mainPop converge better toward the Pareto-optimal front. Extensive experiments on three well-known test suites and a real-world case study fully demonstrate that DD-CMOEA is more competitive than five state-of-the-art CMOEAs.

coevolution. Constrained multi-objective optimization problems Convergence Evolutionary computation exploitation exploration Optimization Search problems Sociology Statistics Switches

SCI ; EI

英语

其他

National Natural Science Foundation of China["61773390","72071205","61876075"] ; Hunan Youth Elite Program[2018RS3081] ; National Science Fund for Outstanding Young Scholars[62122093] ; Scientific Key Research Project of National University of Defense Technology[ZZKY-ZX-11-04] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000862385200027

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20214911286118

Artificial intelligence ; Constrained optimization ; Evolutionary algorithms ; Multiobjective optimization ; Natural resources exploration ; Pareto principle

Artificial Intelligence:723.4 ; Optimization Techniques:921.5 ; Systems Science:961

COMPUTER SCIENCE

2-s2.0-85120551208

Scopus

1.College of Systems Engineering, National University of Defense Technology, and Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology (HKL-MESIT), Changsha 410073, China. (e-mail: mmengjun@gmail.com)
2.College of Systems Engineering, National University of Defense Technology, and Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology (HKL-MESIT), Changsha 410073, China.
3.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

Ming，Mengjun,Wang，Rui,Ishibuchi，Hisao,et al. A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2021,PP(99):1-1.

Ming，Mengjun,Wang，Rui,Ishibuchi，Hisao,&Zhang，Tao.(2021).A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,PP(99),1-1.

Ming，Mengjun,et al."A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION PP.99(2021):1-1.