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This code expains kalmanswarm optimization method.All files have been written on matlab 2007a. This method has been explianed with various benchmark functions. This optimization method can be directly compared with other unconstrained optimization me
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In this paper, a novel Discrete Particle Swarm Optimization Algorithm
(DPSOA) for data clustering has been proposed. The particle positions and velocities
are defined in a discrete form. The DPSOA algorithm uses of a simple probability
approach
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it is the matlab code of particle swarm optimization with a test function that can be change-it is the matlab code of particle swarm optimization with a test function that can be change..
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Flower pollination is an intriguing process in the natural world. Its evolutionary
characteristics can be used to design new optimization algorithms. In this paper, we
propose a new algorithm, namely, flower pollination algorithm, inspired by the
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Using Matlabe wrote PSO optimization algorithm with swarm_data.
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粒子群算法是寻找某个最大值，但是我
们工程上用的目标函数往往是求最小值，因此我把程序改成寻找目标函数的最小值了。
如果需要调回来的话，在函数体里面把max() 改为min()-Particle swarm optimization (pso) algorithm is to find a maximum, but me
The project with the minimum of the objective function is often, so I change the pr
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PSO 单目标粒子群算法用c++语言实现-PSO PSO algorithm with single objective c++ language
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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
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In this work, an algorithm for classical particle swarm optimization (PSO) has been discussed. Also, its
codes in MATLAB environment have been included. The effectiveness of the algorithm has been analyzed
with the help of an example of three varia
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带有不等式/等式约束的加速粒子群算法（apso），主要通过罚函数来进行约束，速度较快，可处理带约束问题(Accelerated particle swarm optimization (APSO) with inequality / equality constraints, which is mainly constrained by penalty function, is fast and can deal with constrained problems)
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