A dual-grid dual-phase strategy for constrained multi-objective optimization

Ming，Mengjun1; Wang，Rui1; Zhang，Tao1; Ishibuchi，Hisao2

10.1109/SSCI44817.2019.9002872

2019-12-01

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

1881-1888

6-9 Dec. 2019

Xiamen, China

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

Institute of Electrical and Electronics Engineers Inc.

Constrained multi-objective optimization problems (CMOPs) appear frequently in engineering applications. In some cases, feasible regions are narrow and/or disconnected. For this kind of problems, existing constraint-handling methods, integrated with multi-objective evolutionary algorithms, are easily stuck at local optima. Aiming to strengthen the global search ability, a dual-grid dual-phase strategy is proposed, which is termed dual-grid push and pull search (DPPS). In the DPPS, two populations, corresponding to dual grids, are used individually to explore the feasible and infeasible spaces. Specifically, one population maintains feasible solutions, and the other explores the whole search space without considering constraints. Then, the two populations share useful information and pull each other so as to enable the algorithm to search for the optimal feasible region (i.e., Pareto solution set). To demonstrate the effectiveness of the proposed algorithm, the MOEA/D integrated DPPS (MOEA/D-DPPS) is tested on a frequently-used benchmark suite as well as a newly-constructed suite. Experimental results clearly show the superiority of MOEA/D-DPPS compared with six state-of-the-art algorithms.

constrained multi-objective optimization problem (CMOP) dual-grid push and pull evolutionary algorithms MOEA/D-DPPS

其他

英语

EI ; CPCI-S

[2017ZT07X386]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467201141

20201108277080

Artificial intelligence ; Constrained optimization ; Evolutionary algorithms

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

2-s2.0-85080889197

Scopus

1.National University of Defense Technology,College of Systems Engineering,Changsha,410073,China
2.Southern University of Science and Technology,Shenzhen Key Laboratory of Computational Intelligence,University Key Laboratory of Evolving Intelligent,Systems of Guangdong Province,Shenzhen,518055,China

Ming，Mengjun,Wang，Rui,Zhang，Tao,et al. A dual-grid dual-phase strategy for constrained multi-objective optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:1881-1888.