AMD-APP-SDKInstaller-v3.0.130.135-GA-windows-F-x64.exe,有例子

伴随着现代物流的快速发展，冷链物流也得到快速发展。在冷链物流研究中配送路径优化问题对冷链物流的发展起到至关重要的作用，鉴于蚁群算法在路径优化问题中的成功应用，因此将蚁群算法应用到冷链物流配送路径优化问题中。考虑到蚁群算法运行中存在的问题，将遗传算法与粒子群算法引入到蚁群算法中，构成基于PSOGAACO算法的冷链物流配送路径优化算法。实验结果表明，这种构想是可行的，可以有效提高算法运行效率，缩短配送距离，提高经济效益。

带时间窗的车辆路径问题 带时间窗（VRPTW）的车辆路径问题的计算后勤。 求解技术，粒子群优化（PSO）算法和遗传算法（GA）在VRPTW中的应用比较回顾。 解决方案算法 解决方案技术算法基于并根据下面给出的相应参考文献进行。 该算法使用Python 3进行编码。 VRPTW的遗传算法： Ombuki，Beatrice，Brian J. Ross和Franklin Hanshar。 “带有时间窗的车辆路径问题的多目标遗传算法。” 应用智能24.1（2006）：17-30。 VRPTW的粒子群优化算法： 龚永杰，张静，刘澳，黄瑞珠，钟HS，史玉华。 使用时间窗优化车辆路径问题：离散粒子群优化方法。 IEEE关于系统，人与控制论的交易，C部分（应用和评论）。 2012年3月； 42（2）：254-67。 数据集：所罗门基准数据集 更新的文件： 对于每种算法和各自的指标，可以在以下文件

技嘉 G41MTS21 BIOS V3版本 技嘉主板BIOS 老主板BIOS

遗传算法优化；神经网络预测‘ 直接使用’

matlab运用GA求解 clc clear %%遗传算法的参数 maxgen=100; %迭代次数 sizepop=50; %种群规模 pcross=0.75; %交叉概率 pmutation=0.25; %变异概率 n=5; %n表示基因的个数 p=10; %p表示权限的个数

最优传输理论的详细数学基础“When I was first approached for the 2005 edition of the Saint-Flour Probability Summer School, I was intrigued, flattered and scared.1 Apart from the challenge posed by the teaching of a rather analytical subject to a probabilistic audience, there was the danger of producing a remake of my recent book Topics in Optimal Transportation. However, I gradually realized that I was being offered a unique opportunity to rewrite the whole theory from a different perspective, with alternative proofs and a different focus, and a more probabilistic presentation; plus the incorporation of recent progress. Among the most striking of these recent advances, there was the rising awareness that John Mather’s minimal measures had a lot to do with optimal transport, and that both theories could actually be embedded in a single framework. There was also the discovery that optimal transport could provide a robust synthetic approach to Ricci curvature bounds. These links with dynamical systems on one hand, differential geometry on the other hand, were only briefly alluded to in my first book; here on the contrary they will be at the basis of the presentation. To summarize: more probability, more geometry, and more dynamical systems. Of course there cannot be more of everything, so in some sense there is less analysis and less physics, and also there are fewer digressions”

本算法为用遗传算法改进粒子群GA-PSO算法，附带含有程序使用说明。(This algorithm uses genetic algorithm to improve particle swarm optimization GA-PSO algorithm, with instructions for the use of the program.)

GA∕Z 1360-2018 信息安全技术 信息安全标准体系表，包括已发布、计划制定、即将发布的标准

Jx-WFST：包装特征选择工具箱 -------------------------------------------------- -------------------------------------------------- -------------------------------------------------- ------------- * 该工具箱提供了 40 多种包装器特征选择方法 * 提供了如何在基准数据集上应用这些方法的示例 * 这个 Jx-WFST 工具箱的详细信息可以在https://github.com/JingweiToo/Wrapper-Feature-Selection-Toolbox找到