文章基本信息

标题：A hybrid of genetic algorithm and Fletcher-Reeves for bound constrained optimization problems
本地全文：下载
作者：Bhunia, A. ; Pal, P. ; Chattopadhyay, S. 等
期刊名称：Decision Science Letters
印刷版ISSN：1929-5804
电子版ISSN：1929-5812
出版年度：2015
卷号：4
期号：2
页码：125-136
DOI：10.5267/j.dsl.2015.1.003
语种：English
出版社：Growing Science Publishing Company
摘要：In this paper a hybrid algorithm for solving bound constrained optimization problems having continuously differentiable objective functions using Fletcher Reeves method and advanced Genetic Algorithm (GA) have been proposed. In this approach, GA with advanced operators has been applied for computing the step length in the feasible direction in each iteration of Fletcher Reeves method. Then this idea has been extended to a set of multi-point approximations instead of single point approximation to avoid the convergence of the existing method at local optimum and a new method, called population based Fletcher Reeves method, has been proposed to find the global or nearer to global optimum. Finally to study the performance of the proposed method, several multi-dimensional standard test functions having continuous partial derivatives have been solved. The results have been compared with the same of recently developed hybrid algorithm with respect to different comparative factors.
关键词：Bound Constrained Optimization problem;Fletcher Reeves method;Genetic Algorithm;Global-optima;Hybrid Algorithm