An Improved Particle Swarm Optimization and Simulation

Particle swarm optimization(PSO) algorithm is a new optimization technique originating from artificial life and evolutionary computation.PSO is easily understood,realized.PSO has few parameters need to be tuned,and has been applied widely.To overcome the problem of premature convergence on PSO,proposes an improved particle swarm optimization(IPSO),which is guaranteed to keep the diversity of the particle swarm and to improve performance of basic PSO algorithm.Three benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,effectively solve the premature convergence problem,but also have good stability.