Incorporating mutation scheme into krill herd algorithm for global numerical optimization

	Incorporating mutation scheme into krill herd algorithm for global numerical optimization
	Wang G. G.; Guo L. H.; Wang H. Q.; Duan H.; Liu L.; Li J.
	2014
发表期刊	Neural Computing & Applications
ISSN	ISBN/0941-0643
卷号	24 期号:3-4 页码:853-871
摘要	Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH 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 cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.
收录类别	SCI ; EI
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/44074
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Wang G. G.,Guo L. H.,Wang H. Q.,et al. Incorporating mutation scheme into krill herd algorithm for global numerical optimization[J]. Neural Computing & Applications,2014,24(3-4):853-871.
APA	Wang G. G.,Guo L. H.,Wang H. Q.,Duan H.,Liu L.,&Li J..(2014).Incorporating mutation scheme into krill herd algorithm for global numerical optimization.Neural Computing & Applications,24(3-4),853-871.
MLA	Wang G. G.,et al."Incorporating mutation scheme into krill herd algorithm for global numerical optimization".Neural Computing & Applications 24.3-4(2014):853-871.

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