Abstract: Particle swarm optimization (PSO) algorithm shows good performance on solving small-scale unconstrained optimization problem, however, it has poor convergence and stability on solving large-scale ones. In order to improve the performance of the PSO algorithms, a global particle swarm optimization (GPSO) algorithm was proposed. The GPSO introduces a new inertia weight, and it is defined as the product of an exponential type function and a random number, which is beneficial to keeping the global and local searching capabilities of the proposed algorithm. On the other hand, the GPSO adds small disturbance to the global optimal solution, which can effectively avoiding the premature problems in the convergence of the GPSO algorithm. Three particle swarm optimization algorithms were used to solve six unconstrained optimization problems. Simulation results demonstrated that the GPSO has faster convergence rate and stronger capability of escaping from the local optimum when compared with the other two existing particle swarm optimization algorithms.