针对标准粒子群算法收敛速度慢和精度低的问题,提出了一种嵌入共轭梯度法的混合粒子群优化算法.算法有效结合了粒子群优化算法较强的全局搜索能力和共轭梯度法快速精细的局部搜索能力,在基本粒子群算法得到的最优解的基础上引入共轭梯度法,加快了算法的收敛速度,克服了基本粒子群算法收敛慢的弊端.相比于基本粒子群算法,它能够以较高精度和较快速度收敛到所求无约束优化问题的全局最优解.数值实验结果表明,所得混合算法是一种求解高维多峰连续函数无约束优化问题的高效方法. Aiming at the problem of slow convergence rate and low precision of standard particle swarm optimization algorithm, a hybrid particle swarm optimization algorithm with conjugate gradient method is proposed. The algorithm effectively combines the global search ability of PSO and the fastness of conjugate gradient method The fine local search ability, based on the optimal solution obtained by the PSO, introduces the conjugate gradient method to speed up the convergence of the algorithm and overcomes the drawback of the slow convergence of the PSO algorithm.Compared with the basic particle swarm optimization , Which can converge to the global optimal solution of the unconstrained optimization problem with higher precision and faster speed.The numerical experiments show that the proposed hybrid algorithm is an efficient method for solving unconstrained optimization problems with high dimensional and multimodal continuous functions .