Chaotic particle swarm optimization algorithm based on the essence of particle swarm

A chaotic particle swarm optimization (CPSO) algorithm based on the essence of PSO was proposed, following an introduction to the studies on the essence of PSO algorithm. The new algorithm uses chaotic search rather than a random number generator to search a promising region. To increase the diversity, the globally best position in standard PSO algorithm is replaced by the center or weighted mean of the personal best positions of several particles in the same neighborhood. The radius of the chaotic searching region is then adaptively adjusted according to the distance between the personal best position of each particle and the center. Several benchmark fonctions were simulated with CPSO, and the results were compared with those obtained with some existing PSO algorithms. The comparison verifies the efficiency of CPSO.