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) is a new stochastic optimization technique originating from artificial life and evolutionary computation.
粒子群优化(PSO)算法是一类新兴的随机优化技术，其思想来源于人工生命和演化计算理论。
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。
A method based on improved binary particle swarm optimization (PSO) is proposed for distribution network reconfiguration with the objective of load balancing.
提出了基于改进的二进制粒子群优化算法、以均衡负荷为目标的配电网重构方法。
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。
An algorithm for discretization based on Particle swarm optimization (PSO) is presented, which can settle the problem of continuous attributes discretization in systema modeling perfectly.
提出了一种基于微粒群优化(PSO)算法的连续属性离散化方法，很好的解决了建模过程中连续属性的离散化问题。
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。
A novel hybrid algorithm approach that employs a particle swarm optimization (PSO) and a multistage detection for the multiuser detection problem (PSOMSD) is proposed.
提出了一种新颖的基于粒子群优化和多级检测的混合算法的多用户检测器。
To determine the optimal thresholds in image segmentation, a new multilevel thresholding method based on particle swarm optimization (PSO) is proposed in this paper.
为确定图像分割的最佳阈值，基于粒子群优化算法提出了一种多阈值图像分割方法。
A new adaptive filtering model based on particle swarm optimization (PSO) algorithm is proposed and designed. It is proved to be efficient and effective in the computer simulation example test.
提出并设计了一种基于粒子群优化算法的振动信号的自适应滤波模型。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。
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。
The paper studies the mathematical model of the dynamic reactive optimization and the particle swarm optimization (PSO).
论文研究了动态无功优化的数学模型和粒子群优化算法。
In allusion to the problem of dynamic self-calibration, a novel self-calibrating algorithm for visual position based on particle swarm optimization (PSO) is suggested in this paper.
针对动态自标定的问题，提出了一种改进的基于粒子群优化(PSO)的自标定位置视觉定位算法。
A particle swarm optimization (PSO) technique based novel approach for solving the dynamic voltage security constrained preventive control problem is proposed.
基于粒子群优化算法(PSO)，提出一种求解含动态电压安全约束的预防控制新方法。
Furthermore, the precision improvement of state estimation due to incorporation of PMU is analyzed based on the model and particle swarm optimization (PSO) is applied to solve the placement of PMU.
基于此模型，本文还进一步分析了引入pmu后对状态估计精度的影响，从而提出了基于粒子群优化算法(PSO)的PMU配置。
A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
Particle Swarm optimization (PSO) is an evolutionary computation technique and an optimization tool based on iteration.
粒子群优化算法(PSO)是一种进化计算技术，是一种基于迭代的优化工具。
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 chaos search based hybrid particle swarm optimization (PSO) algorithm is proposed in the paper to avoid the premature phenomenon of PSO, which is applied into the reactive power optimization.
应用粒子群优化算法(PSO)求解电力系统无功优化问题，提出基于混沌搜索的混合粒子群优化算法，以克服P SO容易早熟而陷入局部最优解的缺点。
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。
This paper presents a new polygonal approximation approach based on the particle swarm optimization (PSO) algorithm.
本文提出一种新的基于多边形逼近算法的粒子群优化算法。
The particle swarm optimization (PSO) technique is applied to find the optimal location of TCSC with maximum system load ability and minimum cost of installation of TCSC.
提出一种采用粒子群优化技术，以系统载荷能力最大化及安装费用最小化为目标，确定TCSC最佳安装位置的方法。
By using Particle Swarm Optimization (PSO), a model was built up for calculating the optimum dispersion height of intrusive submunition dispenser, and mathematical simulation was carried out.
利用粒子群的优化算法，建立侵彻子母弹最佳抛撒高度的求解模型，并进行了仿真计算。
A slope stability evaluation method based on particle swarm optimization (PSO) and least square support vector machine (LSSVM) is proposed.
提出了基于粒子群算法(PSO)和最小二乘支持向量机(LSSVM)的边坡稳定性评价方法。
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。
Particle Swarm optimization (PSO) algorithm is a population-based global optimization algorithm, but it is easy to be trapped into local minima in optimizing multimodal function.
粒子群优化算法应用于多极值点函数优化时，存在陷入局部极小点和搜寻效率低的问题。
Then, this paper carried out in-depth research for particle swarm optimization (PSO) algorithm.
然后，本文对粒子群算法(PSO)进行深入的研究。
It was combined grid theory with Particle Swarm Optimization (PSO).
它是结合网格原理与粒子群算法(PSO)。
It was combined grid theory with Particle Swarm Optimization (PSO).
它是结合网格原理与粒子群算法(PSO)。
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