BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems

被引：19
|
作者：
Li, Xiang [1 ]
Du, Gang [1 ]
机构：
[1] Tianjin Univ, Sch Management, Tianjin 300072, Peoples R China
来源：
COMPUTERS & OPERATIONS RESEARCH | 2013年 / 40卷 / 01期
关键词：
Multi-objective optimization; Constrained multi-objective optimization; Inequality constraint; Constraint handling; Genetic algorithms; Boundary simulation method; Binary search method; Population diversity; Pareto optimum; Pareto set; Pareto front; Trie-tree; Rtrie-tree; Atrie-tree; EVOLUTIONARY ALGORITHMS;
D O I：
10.1016/j.cor.2012.07.014
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Most of the existing multi-objective genetic algorithms were developed for unconstrained problems, even though most real-world problems are constrained. Based on the boundary simulation method and trie-tree data structure, this paper proposes a hybrid genetic algorithm to solve constrained multi-objective optimization problems (CMOPs). To validate our approach, a series of constrained multi-objective optimization problems are examined, and we compare the test results with those of the well-known NSGA-II algorithm, which is representative of the state of the art in this area. The numerical experiments indicate that the proposed method can clearly simulate the Pareto front for the problems under consideration. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：282 / 302
页数：21
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