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分享了社交网络搜索算法源代码及其原文

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.

求解旅行商问题的蚁群优化算法，包含路径的构造、轮盘赌法进行城市的选择、信息素的更新等函数，仅300行代码一个main.cpp即可实现全部功能，程序运行后会输出城市坐标、距离矩阵、迭代后的最优路径及最短路径长度。

一种机器人轨迹规划的优化算法pdf,一种机器人轨迹规划的优化算法

3DMMA拓扑优化程序代码及注释详见博客：https://blog.csdn.net/qq_42183549/article/details/122382792

在学习计算机视觉的过程中，对常见的最优化算法（梯度下降法、牛顿法、高斯牛顿法等）实现的详细原理，在此分享给有需要的同学。

为降低传统仿真优化方法所需的仿真次数，从而缩短仿真优化时间，提出了一种基于GRNN神经网络的仿真优化算法设计。首先，利用仿真生成一定数量的样本集，利用GRNN神经网络进行训练，得到初始回归曲面，并在该曲面上利用Pattern Search算法找出全部可能的局部极小，由于可能会找到一些假局部极小点-即噪声点，设计了剔除噪声点的方法，得到全部局部极小；在各局部极小点周围增补少量仿真样本，再次利用GRNN进行训练，得到新的回归曲面。重复增补样本，直到得到仿真优化的最优解。实例表明，本文方法能够有效降低所需样本的数量，实现仿真优化问题的求解。

分享了Aquila Optimizer:金雕优化器源代码及原文，更多算法可进入空间查看