针对红外图像的火焰识别,采用基于粒子群优化算法的二维最大熵阈值选取方法,选取最佳阈值对红外图像进行分割,使可疑区域从背景中分离出来.选择物体的高度作为特征量,采用标准模板序列,设计两层模糊分类器分析物体的高度变化和灰度分布,给出可疑目标隶属于火焰的评价.实验证明,这种结合火焰动、静特性的算法鲁棒性强,识别率及灵敏度较高,适用于广范围的火灾监控.

针对粒子群算法求解多目标问题极易收敛到伪Pareto 前沿(等价于单目标优化问题中的局部最优解), 并且 收敛速度较慢的问题, 提出一种?? 占优的自适应多目标粒子群算法(??DMOPSO). 在??DMOPSO算法中, 每个粒子的 邻居根据粒子的运行动态地组建, 且粒子的速度不由其邻居中运行最好的粒子来调整, 而是由其所有邻居共同调整. 同时, 采用外部存档保存非劣解, 并利用?? 占优更新非劣解. 模拟结果表明了??DMOPSO算法的有效性.

为进一步提高多目标粒子群算法的收敛性和多样性,提出一种多策略融合改进的多目标粒子群优化算法.首先,引入分解思想以增加Pareto解集的多样性;然后,在速度和位置更新时,引入“多点”变异,即随着迭代次数的递增,根据相应判据对位置的更新作出不同的变异,避免算法早熟现象的发生;最后,将更新后种群和最优解集进行非支配排序,最优解放入精英外部存档.仿真实验结果表明,与另外4种进化算法对比,所提出算法表现出良好的整体性能.

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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。

Matlab

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.

基于改进粒子群算法的永磁同步电机参数识别，陶之雨，张波，在工程应用中，针对提高永磁同步电机参数识别的准确度问题，提出了改进适应度函数的粒子群优化算法。首先建立了包含电流控制和空

为了有效降低纳什均衡求解的复杂度并提高其计算效率，提出了一种粒子群算法近似求解混合战略纳什均衡的新方法。在介绍混合战略纳什均衡理论的基础上，提出了混合战略纳什均衡定义的计算形式，并据此提出了混合战略近似纳什均衡的概念，给出了粒子群算法求解混合战略近似纳什均衡的方法步骤。通过仿真验证了近似纳什均衡理论及粒子群求解过程的正确性，与原粒子群算法进行比较，得到新粒子群算法时效性更强的结论。