Abstract: For complex functions with high dimensions，standard particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optimum.This paper proposes an inertia weight function，which can balance global and local search ability，fasten convergence speed，and by adding the mutation operation to the algorithm in the later phase，this algorithm improves the ability to break away from the local optimum solutions effectively.Experimental results on three typical complex functions with high dimensions show that the modified algorithm can rapidly converge at high quality solutions.

Key words: particle swarm optimization, inertia weight, mutation

摘要： 针对高维复杂函数优化，标准PSO算法收敛速度慢，易陷入局部最优点的缺点，提出一个惯性权重函数使算法的全局与局部搜索能力得到良好平衡，以达到快速收敛；并且该算法通过在后期进行变异操作，有效地增强了算法跳出局部最优解的能力。通过对三个典型的测试函数的优化所做的对比实验，表明改进的算法在求解质量和求解速度两方面都得到了好的结果。

关键词: 粒子群优化, 惯性权重, 变异