Chaotic grey wolf optimization algorithm for constrained optimization problems

被引：305

作者：

Kohli, Mehak ^{[1
]}

Arora, Sankalap ^{[1
]}

机构：

[1] DAV Univ, Jalandhar, India

来源：

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING | 2018年 / 5卷 / 04期

关键词：

Chaotic grey wolf optimization; Firefly algorithm; Flower pollination algorithm; Particle swarm optimization algorithm; PARTICLE SWARM OPTIMIZATION; ENGINEERING OPTIMIZATION; GENETIC ALGORITHMS; FIREFLY ALGORITHM;

D O I：

10.1016/j.jcde.2017.02.005

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems. (C) 2017 Society for Computational Design and Engineering. Publishing Services by Elsevier.

引用

页码：458 / 472

页数：15

共 70 条

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