Author： Wang Gaige Guo Lihong Duan Hong Wang Heqi Liu Luo Shao Mingzhen
Publisher： American Scientific Publishers
ISSN： 1546-1963
Source： Journal of Computational and Theoretical Nanoscience, Vol.10, Iss.10, 2013-10, pp. : 2312-2322
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Abstract
A novel robust hybrid meta-heuristic optimization approach, which can be considered as an improvement of the recently developed biogeography based optimization, namely HSBBO, is proposed to solve global numerical optimization problem. HSBBO combines the exploration of harmony search (HS) with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements and it is demonstrated that, in most situations, the performance of this hybrid meta-heuristic method (HSBBO) is superior to or at least highly competitive with the standard BBO and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, PBIL, PSO and SGA. The effect of the HSBBO parameters is also analyzed.
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