针对标准粒子群算法收敛速度慢和易出现早熟收敛等问题,提出一种高效粒子群优化算法.首先利用局部搜索算法的局部快速收敛性,对整个粒子群目前找到的最优位置进行局部搜索;然后,为了跳出局部最优,保持粒子的多样性,给出一个学习算子.该算法能增强算法的全局探索和局部开发能力.通过对10个标准测试函数的仿真实验并与其他算法相比较,结果表明了所提出的算法具有较快的收敛速度和很强的跳出局部最优的能力,优化性能得到显著提高. Aiming at the problems of slow convergence rate and early premature convergence of standard particle swarm optimization, an efficient particle swarm optimization algorithm is proposed.Firstly, local search of the optimal location found by the whole particle swarm is performed by using local fast convergence of local search algorithm. Then, in order to jump out of the local optimum and maintain the diversity of the particles, a learning operator is given, which can enhance the global exploration and local development ability of the algorithm.By simulating the 10 standard test functions and comparing with other algorithms The results show that the proposed algorithm has a faster convergence rate and a strong ability to jump out of the local optimum, and the optimization performance is significantly improved.