Particle swarm optimization with normal cloud mutation

The particle swarm optimization algorithms converges rapidly during the initial stages of a search, but often slows considerably and can get trapped in local optima. The swarm particle with mutation can speed up convergence and escape local minima. Because normal cloud model has the properties of randomness and stable tendency, this paper proposed a particle swarm optimization with normal cloud mutation (NCM-PSO). This method is tested and compared with the constriction particle swarm optimization (CPSO) with Gaussian mutation (GM-PSO), the CPSO with Cauchy mutation (CM-PSO), and CPSO without mutation. The results show that the proposed method is superior to the others previously mentioned.