Description: 北京大学计算智能研究室几位计算智能教授的辛苦结晶，包含粒子群算法源代码，PPT文档说明，十五个典型的测试函数，算法注释详尽，简明扼要，是难得的粒子群算法学习材料-Institute of Computational Intelligence Computational Intelligence Research Laboratory Professor of several hard crystal that contains the source code for Particle Swarm Optimization, PPT documentation, 15 typical test functions, the algorithm notes detailed and concise, is a rare particle swarm optimization learning materials Platform: |
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Description: 代码主要针对群体智能算法的研究几种不同算法，轨道跟踪的UKF算法。-The code is mainly aimed at the research of swarm intelligence algorithm, and the UKF algorithm of track following. Platform: |
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Description: 受帝国主义殖民竞争机制的启发，Atashpaz-Gargari和Lucas于2007年提出了一种新的智能优化算法—帝国竞争算法 (ICA)。与GA, PSO, ABC等受生物行为启发的群智能算法不同，ICA受社会行为启发，通过摸拟殖民地同化机制和帝国竞争机制而形成的一种优化方法。ICA也是一种基于群体的优化方法，其解空间由称为国家的个体组成。ICA将国家分为几个子群，称为帝国。在每个帝国内，ICA通过同化机制使非最优的国家(殖民地)向最优国家(帝国主义国家)靠近，该过程类似于PSO。帝国竞争机制是ICA的关键，ICA通过帝国竞争机制将最弱帝国中的一个或多个殖民地移动到其他帝国，使帝国之间可以进行信息交互。(Inspired by the imperialist colonial competition mechanism, Atashpaz-Gargari and Lucas proposed a new intelligent optimization algorithm, Empire competition algorithm (ICA), in 2007. With GA, PSO, ABC and other biological behavior of swarm intelligence algorithm inspired by social behavior, ICA heuristic, an optimization method is formed by simulation of colonial assimilation mechanism and competition mechanism of the empire. ICA is also a swarm based optimization approach whose solution space consists of individuals called states. ICA divides the country into several subgroups, called empires. Within each Empire, ICA moves the non optimal country (colony) to the best country (the imperialist state) through the assimilation mechanism, which is similar to PSO. Imperial competition is the key to ICA, and ICA moves one or more colonies of the weakest Empire to other empires through imperial competition, allowing the Empire to interact with each other.) Platform: |
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