Abstract: Considering that the existing particle swarm optimizations (PSO) do not give simultaneously attention to evolution speed and solution's quality, a simplex method based improved particle swarm optimization (SM-IPSO) is proposed in this paper. In SM-IPSO, the conception of multipopulations is adopted, where PSO and SM run on odd populations and even populations, respectively. And a periodical migrating operation between adjacent populations is also introduced in SM-IPSO in order to achieve cooperative search of both PSO and SM for solution space: SM can get away from local converged points by virtue of PSO, and PSO can improve its local exploiting capability under the help of SM. Furthermore, an improved escape method of particle velocities and improved Nelder-Mead SM are proposed in order to enhance the functions of PSO and SM in this paper. Finally, the proposed algorithm is implemented on a Linux cluster system, and experimental results on optimizing five benchmark functions demonstrate its usefulness.