Using a repair genetic algorithm for solving constrained nonlinear optimization problems

被引：6
|
作者：
Bidabadi, Narges [1 ]
机构：
[1] Yazd Univ, Dept Math Sci, Yazd 89195741, Iran
来源：
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES | 2018年 / 39卷 / 08期
关键词：
Genetic Algorithm; Nonlinear Programming; Least Squares;
D O I：
10.1080/02522667.2017.1395146
中图分类号：
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号：
1205 ; 120501 ;
摘要：
Constraint handling is a major concern in using genetic algorithm (GA) to solve constrained optimization problems. In this paper, we use a repair genetic algorithm for solving constrained nonlinear optimization problems. The repair operator is used into a simple GA as a special operator. We present an effective algorithm for solving the repair problem based on nonlinear programming. Experiments using some nonsmooth test problems are presented and compared with the results obtained with the ga function in MATLAB. These results demonstrate the efficiency and robustness of the proposed approach.
引用
收藏
页码：1647 / 1663
页数：17
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