Abstract: The dynamic particle population based particle swarm optimization algorithm(DPPPSO) is introduced,in which the time-variant population size function is constructed,which contains an attenuation term and an undulation term.The attenuation term makes the population decrease gradually when the particles are converging to the optimum in order to reduce the computational cost;the undulation term consists of periodical phases of ascending and descending.In the ascending phase,new particles are randomly produced to avoid the particle swarm being trapped in the local optimal point;while in the descending phase,particles with lower ability gradually die so that the optimization efficiency is improved.The test on four benchmark functions shows that the proposed algorithm effectively reduces the computational cost and greatly improves the global search ability.