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。
In the method, peak is used as fitness function, and the PSO algorithm is used to withdraw blindly from several signals.
采用峰度作为适应度函数，利用粒子群算法对由多个源信号混合而成的信号进行盲抽取。
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。
This paper proposed a novel Particle Swarm Optimization (PSO) hybrid algorithm to improve the optimum speed and performance of the PSO algorithm.
为了提高粒子群算法的寻优速度和寻优精度，提出一种改进的PSO混合算法。
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。
It makes the search space some sub-region, USES the PSO algorithm to optimize in each region, compares these sub - region global optimums and finds out the search space global optimums.
将搜索空间划分成若干个子区域，在各个子区域中均使用标准P SO算法进行寻优，通过比较各个子区域的全局最优解，从而得出整个搜索空间的全局最优。
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。
The experimental results show that the performance of the proposed parallel algorithm is better than that of the standard PSO.
实验结果表明，该并行算法的性能比标准微粒群算法有了很大的提高。
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。
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。
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。
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。
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)。
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。
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。
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。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
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。
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。
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。
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。
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。
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。
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。
应用推荐