Theoretical analysis proves that the M-PSO algorithm keeps convergence.
通过理论分析，证明算法具有良好的收敛性。
This work concentrates on three multi-objective shop schedulings based on PSO algorithm.
本文主要研究了三种基于粒子群优化的多目标车间调度算法。
The basic applications of PSO algorithm and its engineering applications are summarized.
神经网络是一种应用广泛的人工智能技术。
The PSO algorithm is already successfully applied in the optimization of various static environments.
P SO算法已成功地应用于各类静态函数的优化中。
The PSO algorithm is put into estimating solute transport parameters in streams from tracer experiment data.
河流水质模型参数识别，是建立河流水质数学模型、预报河流水质状态变化的基本数据。
Using the PSO algorithm to optimize the on-line PID controller's parameters, desirable control effect is obtained.
通过运用PSO算法对PID控制器参数进行在线调整，使模型参考自适应控制达到理想的控制效果。
Using PSO algorithm in paper's generating is a discrete solution problem, which extends PSO algorithm's application.
PSO算法组卷是一个离散性求解问题，这是对P SO算法应用上的一个扩展。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
实验结果表明该改进微粒群算法可以有效地解决高维数值优化问题。
PSO algorithm is a novel random optimization method based on swarm intelligence which has more powerful ability of global optimization.
PSO算法是一种新型的基于群体智能的随机优化算法，简单易于实现且具有更强的全局优化能力。
For improving the predicting results, two improved PSO algorithm are presented also in this paper: Velocity Mutation PSO and hybrid PSO.
在此基础上，进一步提出了混合粒子群算法和速度变异粒子群算法两种改进算法提高优化性能。
In order to improve its performance, the paper puts forward a hybrid algorithm which blends the PSO algorithm and simulated annealing algorithm.
为了提高优化性能，将粒子群算法和模拟退火算法结合，得到了粒子群-模拟退火混合调度算法。
Combining PSO and ACS, the proposed algorithm can effectively avoid the local optima of PSO algorithm and the blindness search of ant colony algorithm.
该算法通过在粒子群算法中引入蚂蚁算法，可有效克服粒子群算法后期的局部搜索能力差和蚁群算法前期盲目搜索的缺陷。
As for the inadequate of BP neural network, PSO algorithm is used for its optimization, thus creating a hybrid neural network model for flood forecasting.
针对BP神经网络的不足，采用P SO算法对BP神经网络进行优化，建立一个混合的神经网络洪水预测模型。
This paper incorporates chaos optimization algorithm into the PSO algorithm, and propose a new particle swarm optimization algorithm based on chaos searching (CPSO).
文章把混沌优化搜索技术引入到P SO算法中，提出了基于混沌搜索的粒子群优化算法。
To improve the searching performance of Particle Swarm Optimization (PSO), a modified PSO algorithm with flying time adaptively adjusted was proposed and named FAA-PSO algorithm.
为改善粒子群优化算法的搜索性能，提出一种飞行时间自适应调整的粒子群算法(FAA - P SO)。
By introduction of the ideology of population migration, the M-PSO algorithm keeps the convergence and has good performance such as optimization velocity and optimization results.
通过引入人口迁移的思想，在保证算法收敛性的同时，使M - PSO算法具有良好的优化速度和优化效果。
An improved particle swarm optimization (PSO) algorithm was designed. And a weighted ITAE index of turbine speed error was taken as the fitness function of the improved PSO algorithm.
提出了一种新的改进的粒子群优化算法，并以水轮机转速偏差的加权ITAE指标作为改进粒子群优化算法的适应度函数。
Second, an Enhanced Phase Locked Loop (EPLL) control strategy based on improved Adaptive Notch Filtering (ANF) is proposed, controller parameters are optimized using BF-PSO algorithm.
其次，提出了一种基于改进型ANF的三相EPLL控制策略，并用BF-PSO算法对控制器参数进行优化设计。
Aiming at the problem that normal Particle Swarm Optimization (PSO) algorithm can not approach the best position effectively in dynamic environment, this paper proposes a dynamic PSO algorithm.
针对普通粒子群优化算法难以在动态环境下有效逼近最优位置的问题，提出一种动态粒子群优化算法。
Particle swarm Optimization (PSO) algorithm is based on swarm intelligence theory.
粒子群优化(PSO)算法是基于群体智能理论的优化算法。
Particle Swarm Optimization (PSO) algorithm has existed premature convergence for multimodal search problems.
粒子群优化(PSO)算法对于多峰搜索问题一直存在早熟收敛问题。
Considering that the particle swarm optimization (PSO) algorithm is quite simple and easy to implement, it was used to estimate the nonlinear model parameters in this paper.
粒子群算法操作简便、容易实现且全局搜索功能较强，适用于非线性参数估计。
Particle Swarm Optimization(PSO)algorithm is one of embranchments of swarm intelligence.
粒子群优化算法是群体智能中一个新的分支。
The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain.
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的很有效的方法。
To gain optimization parameters of hydro turbine PID governor, this paper interprets the approach of optimization designing that uses the Particle Swarm Optimization (PSO) algorithm.
为了保证获得最优水轮机PID调节器参数，本文研究了利用微粒群优化（PSO）算法进行参数优化设计的新方法。
Based on the swarm intelligence, Particle swarm optimization (PSO) algorithm is a kind of modern optimization method inspired by the research of the artificial life.
粒子群算法是基于群集智能、受到人工生命研究结果的启发而提出的一种现代优化方法。
Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed.
针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed.
针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
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