To improve the search quality of the standard PSO algorithm for solving high-dimensional function, a dynamic particle swarm optimization algorithm is proposed.
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。
The experimental results show that the performance of the proposed parallel algorithm is better than that of the standard PSO.
实验结果表明，该并行算法的性能比标准微粒群算法有了很大的提高。
The standard particle Swarm optimization (PSO) algorithm cannot adapt to the complex and nonlinear optimization process, because the same inertia weight is used to update the velocity of particles.
由于标准粒子群优化(PSO)算法把惯性权值作为全局参数，因此很难适应复杂的非线性优化过程。
Firstly the thesis introduce several improved PSO algorithm and standard PSO which are from basic PSO.
本论文首先介绍了经过基本粒子群算法改进而来的标准粒子群算法以及改进的其他PSO算法。
The experimental results show that the new algorithm has great advantage of convergence property over standard PSO.
通过与其他算法的数值实验对比，表明了该算法具有较快的收敛速度和较好的收敛精度。
Experimental results show that the proposed algorithm not only has great advantages of convergence property over standard PSO, but also avoids effectively being trapped in local optimum.
实验结果表明，该算法具有较快的收敛速度和较高的收敛精度，能有效避免早熟收敛问题。
The new algorithms are tested and compared with the standard PSO. It is proved that a new method is a simple and effective algorithm.
经过与基本粒子群算法比较测试，证实它是一种简单有效的算法。
The new algorithms are tested and compared with the standard PSO. It is proved that a new method is a simple and effective algorithm.
经过与基本粒子群算法比较测试，证实它是一种简单有效的算法。
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