Аннотация: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.
