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A new improved krill herd algorithm for global numerical optimization.

Authors :
Guo, Lihong
Wang, Gai-Ge
H. Gandomi, Amir
H. Alavi, Amir
Duan, Hong
Source :
Neurocomputing. Aug2014, Vol. 138, p392-402. 11p.
Publication Year :
2014

Abstract

Abstract: This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Lévy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods. [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
09252312
Volume :
138
Database :
Academic Search Index
Journal :
Neurocomputing
Publication Type :
Academic Journal
Accession number :
95930563
Full Text :
https://doi.org/10.1016/j.neucom.2014.01.023