针对粒子群优化算法（Particle Swarm Optimization-PSO）存在算法初期容易陷入局部极值、进化后期收敛速度慢和精度低的缺点，提出了一种用非线性函数调整惯性权重和加速系数的粒子群优化算法（nfPSO）。nfPSO通过一个与当前迭代次数相关的非线性函数控制惯性权重和加速系数，从而提高了算法的收敛速度与精度。通过与两个算法对三个基准测试函数的仿真实验结果对比，说明了nfPSO算法具有良好的收敛速度与精度。

To overcome the problems of the particle swarm optimization (PSO)，such as relapsing into local extremum in the beginning stage of evolution，slow convergence and low convergence precision in the late stage， we propose a particle swarm optimization algorithm with nonlinear function (nfPSO)．In the nfPSO，use a nonlinear function of evolution iteration to control the inertia weight and accelerate coefficient．The experiment results demonstrate that the proposed algorithm is better in the convergence velocity and precision．