Neighborhood sharing particle swarm optimization

Abstract

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.

A Novel Approach of Vector Quantization Using Modified Particle Swarm Optimization Algorithm for Generating Efficient Codebook

“…Alberto Moraglio and Julian Togelius [13] proposed an extension of Geometric Particle Swarm Optimization (GPSO), the inertial GPSO (IGPSO), that generalizes the traditional PSO endowed with the full equation of motion of particles to generic search spaces. Yongfang Chu and Zhihua Cui [14] developed an algorithm based on Neighborhood Sharing Particle Swarm Optimization which replaces the individual experience by the neighbor sharing information of current state and proposes the neighbourhood sharing particle swarm algorithm. Yanduo Zhang and Yunchang Zhu [15] proposed a modified centre particle swarm optimization algorithm which the tournament selection operator is introduced to select the evolved particles.…”