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。
PSO algorithm has more superior performance on global optimization and convergence speed.
粒子群优化算法在全局优化及收敛速度方面具有较大优势。
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。
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。
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。
It presents a method of optimum placement of sensors in gearbox based PSO algorithm to solve the problem of sensors layout and localization.
提出了基于粒子群优化的齿轮箱传感器优化配置方法，解决多测点传感器的布置和定位问题。
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。
To solve the problem, a fuzzy clustering based on predator prey PSO algorithm is presented, which is using density function to initialize cluster centre.
为解决此问题，提出一种基于捕食-被捕食的粒子群优化模糊聚类算法且聚类中心采用密度函数初始化。
Aiming at problem that Particle Swarm Optimization (PSO) algorithm falls into local optimum easily, this paper presents a PSO algorithm based on sub-region.
针对粒子群优化(PSO)算法在寻优时容易陷入局部最优的不足，提出一种基于子区域的PSO算法。
Comparing with the traditional PSO algorithm, it possesses more biological characteristics and is much more closed to the real rules of birds swarm's foraging.
与传统粒子群算法相比，它更具有生物特性，更接近于鸟群觅食的真实规律。
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。
Compared with the traditional PSO algorithm, the experimental results show that the proposed algorithm is efficient, and has been applied in practical project.
将改进的算法与传统方法比较，实验证明改进方法是有效的，并已运用于实际工程中。
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)。
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。
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。
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。
Based on service level agreement(SLA) and the Parallel Multi-swarm Cooperative PSO Algorithm designed by the author, a parallel multi-swarm cooperative PSO framework applied to GHPCP is realized.
基于服务级协定和本文设计的并行多群体协作PSO算法实现了一个应用在基于网格的高性能计算平台上的并行多群体协作PSO框架。
The improved PSO algorithm is tested in two models, to show the effects of improvement and its ability in solving the inverse time overcurrent relay coordination problem when DG units are connected.
通过对两个算例的分析可看到改进算法的效果以及粒子群算法解决分布式电源并网情况下反时限过流保护整定问题的能力。
Particle swarm optimization (PSO) algorithm is a new intelligent one with simple principle which is easy to implement.
微粒群算法是一种新型的智能优化算法，有较强的发现最优解的能力，且算法简单，易于实现。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
微粒群优化算法是求解连续函数极值的一个有效方法。
A concrete dam deformation forecasting model is established based on the particle swarm optimization (PSO) algorithm and the traditional multi-statistical regression model.
将粒子群算法引入大坝安全监控领域，并结合多元回归统计模型，建立基于粒子群算法的混凝土坝变形预报模型。
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。
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。
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。
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