A modified particle swarm optimization based on genetic algorithm and chaos

Abstract

Genetic algorithm (GA) and chaos theory is introduced in classical particle swarm optimization (PSO) to overcome its drawback such as being subject to being poor in performance of precision and falling into local optimization. To enhance the searching ability of arithmetic, the modified PSO uses the selection operator of GA to improve the fitness of the particle swarm. To prevent the prematurity of particles, the modified PSO also uses the properties of ergodicity, stochastic property, and regularity of chaos to lead particles' exploration. The experiment results for typical functions show that the modified PSO can improve the performance of precision and avoid the premature convergence.