Abstract: The optimal solution for multi-dimensional search function,the Particle Swarm Optimization (PSO) easily trapped into local optimum,and convergence speed is slow later.The PSO algorithm employing improved k-medoids clustering analysis algorithm (KM-PSO) is proposed.In KM-PSO,the current particles is firstly divided into multi sub-population by improved k-medoids clustering,and PSO with the advantage of the implicit parallel search enhanced optimization performance.It not only exchanges more information among particle,restrains the tendency of premature,but also increases the converging rate and accuracy.Through the experimental results of both analysis and comparison to prove that KM-PSO is superior to original particle swarm optimization algorithm.