pso算法的工具箱，用于粒子群算法求解优化问题，帮助大家更好应用粒子群算法，无需对算法有较深理解即可实验

粒子群算法能有效求解优化问题，其参数少易于实现，这是粒子群算法的matlab实现代码！

此 MATLAB 代码用于论文“Particle swarm optimization implementation for minimum transmission power supply a full-connected cluster for the Internet” 获取论文： http : //ieeexplore.ieee.org/abstract/document/7224573/ 此代码实现了 PSO 算法以优化 WSN 网络中每个节点的传输功率作者：Gabriel Lobão、Felipe Reis、Jonathan de Carvalho 和 Lucas Mendes

这是关于PSO算法的matlab实现.希望对大家有用了.

为了实现对目标位置和速度的精确无源定位，提出了一种基于优化PSO的时差频差联合定位算法。针对传统的PSO算法收敛速度慢，容易出现局部最优，从而导致定位结果不够精确，定位速度慢的情况，引入对惯性权重系数的优化增加其算法的收敛速度，结合自然选择淘汰机理和遗传算法中杂交概念，加强粒子种群的多样性使其达到全局最优的目的。实验结果表明：相对于标准粒子群算法，本文算法在对目标求解时，能快速收敛，不容易陷入局部最优，并且具有很好的定位精度。

压缩文件中包含支持向量机SVM和PSO算法，是matlab中的工具箱，直接加载调用即可，操作简单，比较好用。

Matlab改进PSO算法2-改进pso算法2.rar 继续上传改进PSO算法的文献和Brian Birge的PSO工具箱，这三篇文献都是在工具箱中提到的，貌似都是动态环境中用到的，极值不变情况下的算法还是BPSO，大体写了写自己的理解和问题，大家有兴趣就看看和讨论一下吧。

直接位置更新策略的试变异粒子群优化算法及其在可靠性优化中的应用[J].

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（ PSO）算法和支持向量机（ SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量;其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数;然后利用最优参数优化花粉浓度预测模型;最后，使用优化后的模型对花粉未来24h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（ BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。

This add-in to the PSO Research toolbox (Evers 2009) aims to allow an artificial neural network (ANN or simply NN) to be trained using the Particle Swarm Optimization (PSO) technique (Kennedy, Eberhart et al. 2001). This add-in acts like a bridge or interface between MATLAB’s NN toolbox and the PSO Research Toolbox. In this way, MATLAB’s NN functions can call the NN add-in, which in turn calls the PSO Research toolbox for NN training. This approach to training a NN by PSO treats each PSO particle as one possible solution of weight and bias combinations for the NN (Settles and Rylander ; Rui Mendes 2002; Venayagamoorthy 2003). The PSO particles therefore move about in the search space aiming to minimise the output of the NN performance function. The author acknowledges that there already exists code for PSO training of a NN (Birge 2005), however that code was found to work only with MATLAB version 2005 and older. This NN-addin works with newer versions of MATLAB till versions 2010a. HELPFUL LINKS: 1. This NN add-in only works when used with the PSORT found at, http://www.mathworks.com/matlabcentral/fileexchange/28291-particle-swarm-optimization-research-toolbox. 2. The author acknowledges the modification of code used in an old PSO toolbox for NN training found at http://www.mathworks.com.au/matlabcentral/fileexchange/7506. 3. User support and contact information for the author of this NN add-in can be found at http://www.tricia-rambharose.com/ ACKNOWLEDGEMENTS The author acknowledges the support of advisors and fellow researchers who supported in various ways to better her understanding of PSO and NN which lead to the creation of this add-in for PSO training of NNs. The acknowledged are as follows: * Dr. Alexander Nikov - Senior lecturer and Head of Usaility Lab, UWI, St. Augustine, Trinidad, W.I. http://www2.sta.uwi.edu/~anikov/ * Dr. Sabine Graf - Assistant Professor, Athabasca University, Alberta, Canada. http://scis.athabascau.ca/scis/staff/faculty.jsp?id=sabineg * Dr. Kinshuk - Professor, Athabasca University, Alberta, Canada. http://scis.athabascau.ca/scis/staff/faculty.jsp?id=kinshuk * Members of the iCore group at Athabasca University, Edmonton, Alberta, Canada.