Improved Particle Swarm Optimization

Particle swarm optimization (PSO) is a new evolutionary computation technique, inspired by swarm intelligence of bird flocking, it first introduced by J. Kenndey and R. C. Eberhart in 1995. PSO is simple in concept, few in parameters and easy in implementation, so it has attach great importance to researchers home and abroad and found applications in many areas since its introduced.Several classes of improved PSO are proposed in this paper. The main works of the dissertation can be summarized as follows:First, PSO algorithm randomness is stronger, to improve the PSO algorithm which is a new population based optimization algorithm against trapping into local minima, slowing convergence and low accuracy. A modified PSO algorithm is proposed .A new vector named the average local best position is proposed to replace the local best of the traditional velocity, and the global best position is replaced by the local best position of other particles. One particle can acquire more information of the others to adjust its movement. The results show the effectiveness of the proposed method.Second, a modified PSO algorithm with flying time adaptive adjusted is proposed. The flying time of every particle in this algorithm is adaptive adjusted in space with addition of the evolutionary generations; Thus, the algorithm overcomes the difficulty of the traditional PSO that the particle's ability of searching is decreasing during the last time of iteration, which is caused by the flying time of every particle is fixedon one.