.png){kind=icon}
返回
Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2015.03.016.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Solving constrained optimization problems (COPs) has been gathering attention from many researchers. In this paper, we defined the best fitness value among feasible solutions in current population as gbest. Then, we converted the original COPs to multi-objective optimization problems (MOPs) with one constraint. The constraint set the function value f(x) should be less than or equal to gbest; the objectives are the constraints in COPs. A reverse comparison strategy based on multi-objective dominance concept is proposed. Compared with usual strategies, the innovation strategy cuts off the worse solutions with smaller fitness value regardless of its constraints violation. Differential evolution (DE) algorithm is used as a solver to search for the global optimum. The method is called multi-objective optimization based reverse strategy with differential evolution algorithm (MRS-DE). The experimental results demonstrate that MRS-DE can achieve better performance on 22 classical benchmark functions compared with several state-of-the-art algorithms. (C) 2015 Elsevier Ltd. All rights reserved,
Keyword:
Constrained optimization problems
Reverse model
Multi-objective optimization techniques
Differential evolution
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
PREAI
分享收藏
分享收藏
查看更多内容