An Improved PSO Algorithm Based on Chaos and Population Core

Abstract—particle swarm optimization (PSO) algorithm is often trapped in local optima and low accuracy in convergence. Following an analysis of the cause of the premature convergence, a novel particle swarm optimization algorithm based on neighborhood explored and chaos is proposed, which is called PCC-PSO. Chaos is introduced to initialized the particle’s position to improve the diversity, the population core learning mechanism and global extreme mutation operator is also introduced to enhance the global search ability. Compared with other three improved algorithms, the PCC-PSO converges faster, prevents the premature convergence problem more effectively