布谷鸟搜索
跳跃
算法
局部搜索(优化)
计算机科学
局部最优
偏移量(计算机科学)
数学优化
元启发式
数学
粒子群优化
量子力学
物理
程序设计语言
作者
Hui Xu,Xiang Liu,Jun Su
出处
期刊:Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications
日期:2017-09-01
被引量:38
标识
DOI:10.1109/idaacs.2017.8095129
摘要
Grey Wolf Optimizer (GWO) is a new meta-heuristic optimization. It is inspired by the unique predator strategy and organization system of grey wolves. Since the GWO algorithm is easy to fall into local optimum especially when it is used in the high-dimensional data, an improved GWO algorithm combined with Cuckoo Search (CS) is proposed in this paper. By introducing the global-search ability of CS into GWO to update its best three solutions that are alpha_wolf, beta_wolf and delta_wolf, the search ability of GWO is strengthened, and the shortcoming of GWO is offset. Preliminary experimental analysis validates that, the propsoed CS-GWO algorithm has a stronger global-search ability, and might avoid to fall into the local optimum and jump out of the local optimum in highdimension datasets, compared with both the original GWO algorithm and the original CS algorithm.
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