In order to deal with the problems of the low convergence rate and tending to jump into the local optimum in the
traditional particle swarm optimization, a hybrid particle swarm optimization is proposed based on the average velocity. A
definition of average velocity is presented to characterize the degree of the activity of particle swarm. The inertial weight and
acceleration factors are adjusted by the definition. A switching simulated annealing algorithm and the updating equations of
annealing temperature are designed, such that all the particles can converge into the global optimum faster and jump out of
the local minimum easily. The experiment of searching optimization of three typical functions is given, and the results show
the effectiveness of the proposed algorithm.