对于高维复杂函数，一般粒子群优化算法收敛速度慢，易早熟收敛。

For complex functions with high dimensions, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima.

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针对基本粒子群优化算法对高维函数优化时搜索精度不高的缺陷，提出了一种动态粒子群优化算法。

To improve the search quality of the standard PSO algorithm for solving high-dimensional function, a dynamic particle swarm optimization algorithm is proposed.

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针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。

Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.

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