Balancing Objective Optimization and Constraint Satisfaction in Expensive Constrained Evolutionary Multiobjective Optimization

被引:20
作者
Song, Zhenshou [1 ,2 ]
Wang, Handing [1 ,2 ]
Xue, Bing [3 ]
Zhang, Mengjie [4 ]
Jin, Yaochu [4 ,5 ]
机构
[1] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China
[2] Xidian Univ, Collaborat Innovat Ctr Quantum Informat Shaanxi Pr, Xian 710071, Peoples R China
[3] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington 6140, New Zealand
[4] Bielefeld Univ, Fac Technol, Chair Nat Inspired Comp & Engn, D-33619 Bielefeld, Germany
[5] Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, England
基金
中国国家自然科学基金;
关键词
expensive constrained multiobjective optimization; multiple search modes; surrogate model; Data selection; ALGORITHM; MOEA/D;
D O I
10.1109/TEVC.2023.3300181
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In dealing with expensive constrained multiobjective optimization problems using surrogate-assisted evolutionary algorithms, it is a great challenge to reduce the negative impact caused by the approximate errors of surrogate models for constraints. To address this issue, we propose a Kriging-assisted evolutionary algorithm with two search modes to adaptively reduce the utilization frequency of surrogate models for constraints. To be more specific, an adaptively switching strategy analyzing the correlation between the objective optimization direction and constraint satisfaction direction is designed to determine whether to build the constraint surrogate models to assist the current evolutionary search. Accordingly, the proposed algorithm contains two search modes: 1) unconstrained surrogate-assisted search mode and 2) constrained surrogate-assisted search mode. In the first search mode, an existing surrogate-assisted evolutionary algorithm without considering constraint is introduced, which rapidly drives the population to move to the feasible region(s) while avoiding the negative effects of the constraint surrogate models. In the second search mode, a novel Kriging-assisted constrained multiobjective optimization algorithm is designed for locating constrained Pareto front in the feasible region. In addition, a data selection strategy is proposed to improve the efficiency and quality of surrogate models for constraint functions. The proposed method has been tested on numerous instances from three popular benchmark test suites. The experimental results demonstrate that the performance of the proposed algorithm outperforms other state-of-the-art methods.
引用
收藏
页码:1286 / 1300
页数:15
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