.png){kind=icon}
返回
An efficient genetic algorithm for solving nonlinear optimization problems defined with fuzzy relational equations and max-Lukasiewicz composition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2018.04.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We study a nonlinear optimization problem with a system of fuzzy relational equations as its constraints. We firstly investigate the resolution of the feasible region when it is defined with max-Lukasiewicz composition and present some necessary and sufficient conditions for the feasibility and some procedures for simplifying the problem. Since the feasible solution set of the fuzzy relational equations (FRE) is non-convex and the finding of all minimal solutions is an NP-hard problem, conventional nonlinear programming methods may involve high computational complexity. Based on the theoretical properties of the problem, a genetic algorithm (GA) is presented, which preserves the feasibility of new generated solutions. The proposed GA does not need to initially find the minimal solutions. Also, it does not need to check the feasibility after generating the new solutions. Moreover, we present a method to generate feasible max-Lukasiewicz FREs as test problems for evaluating the performance of our algorithm. The proposed method has been compared with some related works. The obtained results confirm the high performance of the proposed method in solving such nonlinear problems. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Fuzzy relational equations
Nonlinear optimization genetic algorithm
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:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
分享收藏
On the relation between fuzzy max-Archimedean t-norm relational equations and the covering problem
PREAI
分享收藏分享收藏
查看更多内容