Solving constrained optimization problems using a novel genetic algorithm

被引：35
|
作者：
Tsoulos, Ioannis G.
机构：
[1] Department of Communications, Informatics and Management, Technological Educational Institute of Epirus
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2009年 / 208卷 / 01期
关键词：
Constrained optimization; Evolutionary algorithms; Genetic algorithms; Genetic operations; Stopping rules; NEURAL-NETWORKS; MINIMIZATION; WALKING;
D O I：
10.1016/j.amc.2008.12.002
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A novel genetic algorithm is described in this paper for the problem of constrained optimization. The algorithm incorporates modified genetic operators that preserve the feasibility of the trial solutions encoded in the chromosomes, the stochastic application of a local search procedure and a stopping rule which is based on asymptotic considerations. The algorithm is tested on a series of well-known test problems and a comparison is made against the algorithms C-SOMGA and DONLP2. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码：273 / 283
页数：11
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