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A chaotic particle-swarm krill herd algorithm for global numerical optimization

Gai-Ge Wang (Jiangsu Normal University, Xuzhou, China)
Amir Hossein Gandomi (University of Akron, Akron, Ohio, USA)
Amir Hossein Alavi (Michigan State University, East Lansing, Michigan, USA)


ISSN: 0368-492X

Article publication date: 24 June 2013




To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues.


In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability.


This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH.


Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.



Wang, G.-G., Hossein Gandomi, A. and Hossein Alavi, A. (2013), "A chaotic particle-swarm krill herd algorithm for global numerical optimization", Kybernetes, Vol. 42 No. 6, pp. 962-978.



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