Chaotic cuckoo search

被引：1

作者：

Gai-Ge Wang

Suash Deb

Amir H. Gandomi

Zhaojun Zhang

Amir H. Alavi

机构：

[1] Jiangsu Normal University,School of Computer Science and Technology

[2] Northeast Normal University,Institute of Algorithm and Big Data Analysis

[3] Northeast Normal University,School of Computer Science and Information Technology

[4] Cambridge Institute of Technology,BEACON Center for the Study of Evolution in Action

[5] Michigan State University,School of Electrical Engineering and Automation

[6] Jiangsu Normal University,Department of Civil and Environmental Engineering

[7] Michigan State University,undefined

来源：

Soft Computing | 2016年 / 20卷

关键词：

Global optimization; Cuckoo search; Chaotic maps ; Multimodal function;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.

引用

页码：3349 / 3362

页数：13

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