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记录计算智能优化算法的学习笔记,通过阅读论文并复现的形式加深对相关的启发式智能优化的理解。

License: GNU General Public License v3.0

MATLAB 65.23% Python 34.77%
particle-swarm-optimization ant-colony-algorithm wolf-pack-algorithm firefly-algorithm harris-hawks-optimizer sparrow-search-algorithm

computational-intelligence's Introduction

Computational-intelligence

记录计算智能优化算法的学习笔记,通过阅读论文并复现的形式加深对相关的启发式智能优化的理解。

论文复现目前有matlabpython版,由于个人能力有限,因此难以避免论文代码复现中存在的错误,如有相关不妥之处,还望指正。

在每篇的笔记当中,存在部分个人的观点,该观点仅供参考!

内容目录

单目标优化

粒子群优化

蚁群算法

遗传算法

狼群算法

哈里斯鹰优化算法

麻雀算法

萤火虫算法

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computational-intelligence's Issues

关于“一种新的基于Sigmoid函数的自适应加权粒子群优化器”论文中粒子之间距离计算的问题。

您好!
首先非常感谢您能够整理出关于“一种新的基于Sigmoid函数的自适应加权粒子群优化器”论文中的**,并基于论文复现了Python代码,这对我起到了非常重要的帮助。
其次我也注意到您在介绍这篇论文关于粒子距离计算时,给出了您的三种理解,我对你的直觉非常敬佩。不过在论文中的"IV Experimental Results and Discussion"章节中,指出了该文使用的是欧氏距离。
原文为“It is worth poingting out that the Euclidean distance is chosen as the distance metric in this paper.”
最后依旧非常感谢您的工作和分享。

关于狼群算法中探狼游走的更新公式

同学,你好,有点小疑问,看来你matlab中代码关于探狼游走的更新公式: XJ_every_h = wolf_position(detective_wolf_index(j),:) + sin(2pi(1:h)'/h) .* step_a; 假设是在二维空间,个体狼坐标 [x, y], 更新公式后XJ = [x, y] + [0.1, 0.3, 0.4]’(假设这是sin后求出的值) = [x+0.1 y+0.1; x + 0.3 y + 0.3; x+0.4 y+0.4] ,那这个是不是意味着个体狼始终在一个与y =x平行的直线上来回运动搜索?并没有实现h个方向的搜索,不知道我有没有表达清楚,希望得到你的回复。

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