粒子群优化的回声状态神经网络，粒子群算法是通过模拟鸟群觅食行为而发展起来的一种基于群体协作的随机搜索算法。通常认为它是群集智能 (Swarm intelligence, SI) 的一种。

粒子群算法的入门代码，用C++实现了粒子群算法的基本过程，适合入门

粒子群算法（PSO）Matlab编程版，包括了线性递减惯性因子粒子群算法（PSO）Matlab编程版，

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.

hslogic算法仿真-PSO粒子群优化算法——对多个函数进行最优值搜索

带收缩因子的PSO优化算法 c1 = 2; % 学习因子1 ，一般在[0,2] c2 = 2; % 学习因子2 ，一般在[0,2] % c1 = 2.04344; %学习因子1 ，一般在[0,2] % c2 = 0.94874; %学习因子2 ，一般在[0,2] k1 = 0.7298; % 收缩因子 Dimension = 2; % 搜索空间维数（未知数个数） Popsize = 20; % 初始化群体个体数目 MaxDT = 100; % 最大迭代次数 DivH = 0.25; % 最大多样性系数 DivL = 0.0005; % 最小多样性系数

TSP-PSO %% 与个体最优进行交叉 c1=round(rand*(n-2))+1; %在[1,n-1]范围内随机产生一个交叉位 c2=round(rand*(n-2))+1; while c1==c2 c1=round(rand*(n-2))+1; %在[1,n-1]范围内随机产生一个交叉位 c2=round(rand*(n-2))+1; end chb1=min(c1,c2); chb2=max(c1,c2); cros=Tour_pbest(i,chb1:chb2); %交叉区域矩阵 ncros=size(cros,2); %交叉区域元素个数

通过利用最大类间方差法（OTSU）作为目标函数，结合智能优化算法中的粒子群优化算法（PSO）,来获得图像分割的多个阈值，且阈值个数可设定，效果较好。

python实现PSO算法优化二元函数，具体代码如下所示： import numpy as np import random import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D #----------------------PSO参数设置--------------------------------- class PSO(): def __init__(self,pN,dim,max_iter): #初始化类 设置粒子数量 位置信息维度 最大迭代次数 #self.w = 0.8 self.