A Particle Swarm Optimization based on Chaotic Neighborhood Search to Avoid Premature Convergence

摘要

Particle swarm optimization (PSO) is a good optimization algorithm, but it always premature convergence to local optimization, especially in some complex issues like optimization of high-dimensional function. In this paper, a particle swarm optimization based on chaotic neighborhood search (PSOCNS) is proposed. When the sign of premature convergence is arise, search each small area which is defined of all particles by chaotic search, then jump out of local optimization, and avoid premature convergence. Finally, the experiment results demonstrate that the PSOCNS proposed is better than the basic particle swarm optimization algorithm in the aspects of convergence and stability.