A Modified Particle Swarm Optimization Algorithm and Application

In order to deal with the problem of slow search speed and premature convergence, a flexible particle swarm optimization algorithm is proposed. Simulations have been done to illustrate that this algorithm can not only significantly speed up the convergence, but also effectively solve the premature convergence problem. Furthermore, the algorithm is applied to neural network´s training in the agent model in comparison shopping and the simulation experiment not only shows that compared with related algorithms, the hybrid algorithm which is based on the flexible particle swarm optimization and BP algorithm can quickly converge to a reasonably good solution, but also makes the agent model in comparison shopping more effectively.