Abstract: The particle swarm optimization (PSO) algorithm is easy to trapped into local extremum, and its convergence speed is lower and the precision is worse in the late evolution. Furthermore, the parameter selection can affect the algorithm. Aimed at these disadvantages of PSO,based on using the crossbreeding concept in the genetic algorithm for reference, the new algorithm by introducing dynamical parameters in the evolution of the speed equation is proposed. The convergence speed and the convergence rate were improved. The new method arc tested by function Levy No. 5 shows that the convergence speed and the average convergence rate was increased.

Key words: Particle swarm optimization,Optimization,Crossbreeding,Dynamic parameter