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.
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页码：3349 / 3362
页数：13
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[1]  
Cai X(2012)Light responsive curve selection for photosynthesis operator of APOA Int J Bio-Inspir Comput 4 373-379
[2]  
Fan S(1996)Ant system: optimization by a colony of cooperating agents IEEE Trans Syst Man Cybern B Cybern 26 29-41
[3]  
Tan Y(2012)Krill herd: a new bio-inspired optimization algorithm Commun Nonlinear Sci Numer Simul 17 4831-4845
[4]  
Dorigo M(2011)Mixed variable structural optimization using firefly algorithm Comput Struct 89 2325-2336
[5]  
Maniezzo V(2001)A new heuristic optimization algorithm: harmony search Simulation 76 60-68
[6]  
Colorni A(2014)A new improved krill herd algorithm for global numerical optimization Neurocomputing 138 392-402
[7]  
Gandomi AH(2011)An effective memetic differential evolution algorithm based on chaotic local search Inf Sci 181 3175-3187
[8]  
Alavi AH(2014)Chaotic swarming of particles: a new method for size optimization of truss structures Adv Eng Softw 67 136-147
[9]  
Gandomi AH(2012)Application of differential evolution algorithm on self-potential data PLoS One 7 e51199-97
[10]  
Yang X-S(2015)Modified cuckoo search algorithm with self adaptive parameter method Inf Sci 298 80-1877
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