粒子群优化算法

总结了粒子群优化算法的基本应用，并概述了其在工程优化领域的应用。

The basic applications of PSO algorithm and its engineering applications are summarized .

该方法首先运用粒子群优化算法快速搜索到一个较优的映射矩阵，然后将待隐藏的信息通过该映射进行置换；

This method first employs the PSO algorithm to get the optimal substitution matrix for transforming the secret messages .

改进协同粒子群优化算法及其在FlowShop调度中的应用

An Improved Cooperative Particle Swarm Optimization and Its Application to Flow Shop Scheduling Problem

基于Java多线程技术实现的粒子群优化算法

Asynchronous Pattern of Particle Swarm Optimization Realized with Java Multiple Threads

基于粒子群优化算法的PID液位控制

PID liquid level control based on particle swarm optimization algorithm

基于改进粒子群优化算法的多机器人合作Q学习

Cooperative Q-learning for multi-robots based on improved particle swarm optimization

预测RNA二级结构离散粒子群优化算法

Discrete particle swarm optimization for RNA secondary structure prediction

基于粒子群优化算法和改进的Snake模型的图像分割算法

Image segmentation algorithm based on the PSO and improved Snake model

并把改进的粒子群优化算法和BP神经网络相结合，应用于变压器故障检测中。

Otherwise , the authors combined MDPSO and BP neural network and applied it to the diagnosis of power transformer .

蚁群&粒子群优化算法混合求解TSP问题

Solving TSP Problems by Combining Ant Colony Algorithms and Particle Swarm Optimization

接着应用粒子群优化算法同样研究了合肥光源改造储存环线性lattice。

Then the PSO algorithm is applied to study the linear lattice of the HLS - ⅱ storage ring .

基于粒子群优化算法的WSNs节点定位研究

Research on node localization based on particle swarm optimization for WSNs

一种粒子群优化算法源程序，这是一个VB语言编制的源程序，很实用。

A particle swarm optimization algorithm source code , this is a VB source languages , it is practical .

本文主要研究该算法在Web数据挖掘中的应用，介绍了粒子群优化算法进行Web数据挖掘的基本原理，分析了其特点。

This paper mainly studies the algorithm in the application of Web data mining , introducing the fundamentals of Particle Swarm Optimization algorithms for Web data mining and analysis of its characteristics .

实验表明，参数优化采用粒子群优化算法（PSO）能取得较好的效果。

Experimental results show that parameter optimization using particle swarm optimization ( PSO ) can achieve good results .

反馈控制算法是偏振模色散的自适应补偿器的关键组成部分，将粒子群优化算法（PSO）引入到偏振模色散自适应补偿系统中。

The feedback control algorithm is the key integral part of an adaptive polarization mode dispersion ( PMD ) compensator .

本文提出利用现代启发式方法中的粒子群优化算法（PSO）来解算无功优化问题。

In this paper , particle swarm optimization ( PSO ) was employed to deal with the problem of VAR planning .

研究粒子群优化算法（PSO）的拓扑结构和信息流动，以提高算法性能是PSO的一个有意义的研究方向。

It makes sense to search on the PSO from its topology and information flow in order to improve its performance .

将边界变异操作引入到量子粒子群优化算法中，提出基于边界变异的量子粒子群优化算法（QPSO）B。

This paper introduces a bounded mutation operator into Quantum-behaved Particle Swarm Optimization ( QPSO ) algorithm and proposes QPSOB .

粒子群优化算法（ParticleSwarmoptimization，PSO算法）源于鸟群和鱼群群体运动行为的研究，是一种新的群体智能优化算法，是演化计算领域中的一个新的分支。

Particle swarm optimization ( PSO ) is an evolutionary computation technique developed by Dr. Eberhart and Dr. Kennedy in 1995 , inspired by social behavior of bird flocking or fish schooling .

因此，我们提出了基于离散粒子群优化算法和决策树的SVM多类分类方法。

Therefore , a improved classification algorithm is proposed which is based on Discrete Particle Swarm Optimization algorithm and Decision Tree Method in light of the weakness of the present classification algorithm .

利用Alopex改进的粒子群优化算法及其在软测量建模中的应用

Improved Particle Swarm Optimization Algorithms by Alopex and Its Application in Soft Sensor Modeling

本文旨在对BP神经网络和粒子群优化算法进行理论和应用分析，对粒子群算法的工作机制进行改进，已达到提高算法搜索精度和速度的目的。

This article try to improve the PSO mechanism , in order to achieve increased speed and accuracy purposes , based on the theory and application of comprehensive analysis of the BP neural network and the PSO .

首先介绍了粒子群优化算法（PSO）的基本原理，根据实验提出了改进措施，增强了粒子群优化算法的全局寻优能力。

The basic principles of PSO is introduced in this paper , and some measures are applied into it to enhance the ability finding the most opti - mistic result .

将粒子群优化算法（PSO）应用到电力系统无功优化问题的研究中，给出了具体的实施流程。

The particle swarm optimization algorithm ( PSO ) applied to reactive power optimization in power system is introduced in the paper , and the detailed implementing procedure is presented .

为了设计高效的软件缺陷预测模型，提出一种将粒子群优化算法与朴素贝叶斯（NB）相结合的方法。

In order to design effective software defect prediction model , this paper proposes an approach combining Particle Swarm Optimization ( PSO ) algorithm and Naive Bayes ( NB ) .

本文针对Pso的缺点和多目标优化的特点，提出一些新的机制和策略，改进了基本粒子群优化算法。将改进后粒子群优化算法用于多目标优化问题。

According to the shortcomings of PSO and the characteristics of multi-objective optimization , this paper puts forward some new mechanisms and strategies and improves the elementary particle swarm optimization algorithm .

粒子群优化算法（ParticleSwarmoptimization）是群集智能领域一个很重要的分支，通过种群间个体协作与竞争来实现对问题最优解的搜索。

Particle Swarm Optimization ( PSO ) is an important branch of swarm intelligence . PSO is realized by the organic social behaviors , and by the cooperation and competition among the individuals themselves to search the optimum of the problem .

本文将提出的两种改进粒子群优化算法用于训练人工神经网络，通过实验对比分析，证明了改进粒子群优化算法在训练人工神经网络时可以有效克服BP算法所存在的不足。

The thinking of the two improved PSO algorithms has been used to train artificial neural network in this paper . It has been proved by experiments that the two improved PSO algorithms have more classification effect than BP algorithm .

提出了一种基于自适应型粒子群优化算法（APSO）的自动发电控制（AGC）机组调配方案。

An adaptive particle swarm optimization ( APSO ) algorithm to solve the AGC unit dispatch problem in power system is proposed .