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粒子群算法演示程序

于 2022-09-08 发布 文件大小:9.73 kB
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MATLAB 粒子群单目标优化程序,简单易懂!这个程序能反映出粒子群算法的基本原理。相关解读可由相关的matlab论坛寻找。粒子群优化算法(panicle swarm optimization,PSO)是kennedy和Eberhart在研究鸟类和鱼类的群体行为基础上于1995年提出的一种群智能算法,其思想米源予人工生命和演化计算理论,模仿鸟群飞行觅食行为,通过鸟集体协作使群体达到最优。1.粒子群算法的原理PSO中,每个优化问题的解看作搜索空间中的一只鸟(即粒子),所有的粒子都有一个被优化的函数决定的适应值,并且有一个速度决定它们飞翔的方向和速率,粒子们追随当前的最优粒子在解空间中搜索。算法首先初始化一群随机粒子,然后通过迭代找到最优解。在每一次迭代中,粒子通过跟踪两个“极值”即个体极值和全局极值来更新自己的速度与位置。在D维目标搜索空间中,由种群数为m的粒子组成粒子群,其中第f个粒子在第d维的位置为Xid,其飞行速度为Vid,该粒子当前搜索到的最优位置为Pid(goodvalue)和整个粒子群当前的最优位置Pgd(bestvalue)。

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