粒子群优化

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

粒子群优化（PSO：ParticleSwarmoptimization）算法是一种有效的全局优化技术。

PSO ( Particle Swarm Optimization ) is an efficient stochastic global optimization technique .

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

基于粒子群优化的模糊神经PID的发电机励磁控制研究

Research on Generator Fuzzy Neural PID Based on PSO Excitation Control Strategy

DSP并行系统的并行粒子群优化目标跟踪

Particle swarm optimizer tracking based on DSP parallel system

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

利用粒子群优化（PSO）算法进行水库的优化调度。

The use of PSO algorithm for optimal operation of the reservoir .

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 .

提出了一种基于改进的粒子群优化（PSO）算法的PID控制器参数整定方法。

A modified particle swarm optimization ( PSO ) algorithm is proposed for tuning PID controller parameters .

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

基于粒子群优化的蚁群算法在TSP中的应用

Application of an Ant Colony Algorithm in TSP Based on Particle Swarm

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

基于粒子群优化的移动机器人SLAM方法

Mobile Robot SLAM Method Based on Particle Swarm Optimization

求解连续Minimax问题的粒子群优化方法

Particle Swarm Optimization Method for Solving Continuous Minimax Problems

基于粒子群优化的鲁棒Minimax估计

Robust Minimax Estimation Based on Particle Swarm Optimization

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 .

将该算法应用于4个典型复杂函数的仿真实验，并与标准BP算法以及传统的基于粒子群优化BP网络算法的仿真结果进行比较。

The algorithm is applied to the simulation experiment on several complex functions , and compared it with standard BP network and traditional PSO based BP network training algorithm .

粒子群优化（ParticleSwarmoptimization）算法是一类启发式随机全局优化技术，是一种基于群智能（SwarmIntelligence）方法的演化计算技术。

Particle swarm optimization ( PSO ) algorithm is a Heuristic stochastic global optimization technique , and also PSO algorithm is an evolutionary computation technique based on swarm intelligence method .

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（PSO）算法是一种源于人工生命和演化计算理论的优化技术。

Particle swarm optimization ( PSO ) algorithm as one of optimization techniques comes from artificial life and theory of evolutionary algorithms .

介绍粒子群优化（PSO）算法，并在此基础上利用带惯性权重因子的全局版PSO算法对结构模型修正和损伤检测等约束优化问题进行研究。

The Particle Swarm Optimization ( PSO ) algorithm is briefly introduced and then applied in structural model updating and structural damage detection .

光学薄膜BRDF五参数模型的粒子群优化

Five Parameters Optimization of BRDF Model of Optical Film by Particle Swarm Algorithms