Abstract: Particle swarm optimi}ation(PSO) easily falls into the stagnation at the late evolutionary period because it does not know about the characteristics of the objective function completely. In the classic PSO, the finite information, such as the velocity v(t)，the location x(t)，the individual extremum P} of the particle and the global extremum Pg of the swarm at the prior time t, is employed to drive the evolutionary process. But in the evolutionary of PSO, the distribution characteristics of solutions of the objective function are hidden in the many and many function evaluations while the evo- lutionary is iterating. hhe novel PSO based on evolutionary lcarning(I= PSO ) balances the exploration and the exploita- lion process and controls the r}initialization and crossover selection of particles through the distribution characteristics of solutions extracted statically from the historical evaluations. The experimental results show that the L-PSO can im- prove the precise of solution and reduce the expected iterations although the time and space complexity is increased lightly.

Key words: Particle swarm optimization,Evolutionary process learning,Distribution characteristics,Intelligent particle