Abstract: Particle swarm optimization (PSO) algorithm is a new promising swarm intelligence optimization technology, and it has been extensively applied to solve all kinds of complex optimization problems because of its advantages of simpler theory, less parameter and better performance. However, each particle's individual minimum and population's minimum are two major factors to affect the algorithm's convergence speed and precision. This paper proposes a double center particle swarm optimization algorithm (DCPSO). We analyze particle's flying trajectory and introduce general center particle and special center particle in the DCPSO, which consequently improves PSO algorithm's converging speed and precision without increasing computing complexity. Six classical test functions, including Rosenbrock, Rastrigrin and so on, are used to verify the proposed algorithm in two ways: fixed iteration number test and fixed time length test. Experimental results show that the proposed algorithm is feasible and efficient.