To the problems of low searching speed and premature convergence frequently appeared in standard particle
swarm optimization(PSO) algorithm, an efficient particle swarm optimization(AEPSO) is proposed in this paper. The method
makes full use of the local convergent performance of the local random search algorithm to optimize the global best position
of the swarm found so far. Then to go out of the local optimum in PSO and maintain the population diversity in the process
of evolution, a learning operator is presented. This algorithm can enhance the exploration and exploitation ability of the
algorithm. Through testing the performance of the proposed approach on a suite of 10 benchmark functions and comparing
with other meta-heuristics, the result of simulation shows that the proposed approach has better convergence rate, great
capability of preventing premature convergence and superior performance.