为了有效解决粒子群优化算法易陷入局部最优的缺陷，在粒子群优化算法（PSO）的基础上引入莱维飞行，提出了一种基于莱维飞行的粒子群优化算法（LPSO）。该算法在迭代过程中对粒子位置进化效果进行判断，若粒子多次迭代后仍无法进化到更优位置，则使用莱维飞行更新粒子位置。改进后的算法增加了粒子位置变化的活力，提高了算法的有效性。仿真实验结果表明，该算法在求解全局最优时，效果优于原始粒子群优化算法，在多峰值函数优化问题中其优越性更加突出。

粒子群算法的思想源于对鸟群捕食行为的研究．模拟鸟集群飞行觅食的行为，鸟之间通过集体的协作使群体达到最优目的，是一种基于Swarm Intelligence的优化方法。

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.

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.

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

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。

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结合小生境思想及灾变原理，提出了一种动态调整种群结构的粒子群算法（AGPSO）。该算法在获取局部最优区域后只留下部分粒子寻找局部最优点，同时将其他粒子进行灾变处理，然后约束在剩余区域进行新最优区域搜索，这样既达到了快速局部收敛的目的，同时又增加了粒子种群的多样性，较好地解决了早熟收敛的问题。通过典型优化函数的仿真实验验证了该算法的有效性。