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.
算例证实了算法的有效性，通过和基本粒子群算法比较显示其优越性。
Study cases showed the validity of the algorithm. The compare with basic particle swarm algorithm showed its superiority.
为了解决基本粒子群算法不易跳出局部最优的问题，提出了一种协同粒子群优化算法。
To solve the problem that particle swarm optimization algorithm is apt to trap in local optimum, a novel cooperative particle swarm optimization algorithm is proposed.
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。
Firstly the thesis introduce several improved PSO algorithm and standard PSO which are from basic PSO.
在基本粒子群算法的基础上，引入了惯性因子，并对离散变量进行了处理，直接构造了离散解值集和离散速度值集。
Based on the PSO, we introduce the inertia factor and construct the discrete position set and the discrete speed set, express the discrete variables accurately.
简要介绍了基于模拟退火思想的粒子群算法的基本原理，并将之应用于盲源分离算法中，以解决基本粒子群算法收敛速度缓慢的问题。
The basic principal of particle swarm optimization based on simulate anneal was introduced and was applied to blind source separation to solve the problem of low searching speed.
介绍基本粒子群优化算法的原理、特点，并在此基础上提出了一种改进的粒子群算法。
This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and puts forward an improved particle swarm optimization algorithm.
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.
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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