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。
Aiming at the optimization problem in engineering, this paper proposes a new algorithm for con-strained optimization problem by combining PSO with death penalty.
针对工程中的优化问题，将粒子群算法与死亡罚函数法相结合，提出一种求解有约束问题的优化算法。
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 Particle Swarm Optimization (PSO) algorithm was used to deal with task assignment problem in multi-suppliers' participation in collaborative product development.
通过粒子群最优化算法解决多供应商参与协同产品开发时的任务指派问题。
Particle Swarm Optimization (PSO) algorithm for solving multiple Nash equilibrium solutions of bimatrix game is presented in this paper.
提出了一种求解双矩阵对策多重纳什均衡解的粒子群优化算法。
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。
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。
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。
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。
It makes a searching for all local optimization of the multimodal function that a PSO algorithm based on chaos sequence for multi-modal function optimization.
基于混沌序列的多峰函数微粒群寻优算法的目标就是找到多峰函数的所有局部优化峰值。
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)。
Based on PSO, a new PID control method with incomplete derivation based on particle swarm optimization algorithm is proposed.
以P SO算法为基础，提出了一种新的粒子群优化不完全微分pid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。
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 parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
改进的粒子群优化算法全局搜索BP神经网络的权值和阈值。
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。
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。
As a representative swarm-intelligence based optimization algorithm, Particle SwarmOptimization (PSO) algorithm is applied to capacitor optimization in the dissertation.
微粒群优化算法（PSO）是目前备受关注的群集智能算法的代表性方法，也是本文研究工作的算法基础。
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。
PSO has been proved to be an effective global optimization algorithm. It is easy to implement, quickly convergence, and has been successfully applied to many engineering fields.
粒子群算法已经被证明是一种有效的全局优化算法，其收敛速度快，易于实现，已经成功地运用到了许多工程领域。
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。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
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。
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。
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。
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。
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