Biological results suggest that information provided by neighborhood of each individual offers an evolutionary advantage, furthermore, the current state of neighbors significantly impact on the decision process of group members. However, particle swarm algorithm, as a simulation of group foraging behavior, does not introduce the neighborhood sharing information into its evolutionary equations. Hence, this paper replaces the individual experience by the neighbor sharing information ofcurrent state and proposes the neighborhood sharing particle swarm algorithm In order to verify the performance of the algorithm, five typical high dimensional multimodal functions are selected and the simulation results show that the proposed algorithm is not only superior to the standard version, but also much better than the other two variants.Index Terms-Multimodal test functions, neighborhood, neighborhood sharing particle swarm optimization (NSPSO), particle swarm optimization (PSO), information sharing mechanism.

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