The ACO algorithm employs multi agent called Ants that are capable of finding optimal solution for given problem instance. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to
: Instance代表的数据采自 J.E.Beasley的OR-Library Instances For Set Covering (OR-Library) 这个结果已经远远超过了蚁群算法(ACO)的效果: 以及效果更好的超启发的蚁群算法(ACO-HH) Black Hole Algorithm 暂时把伪代码贴上,有精力的时候再补充和解释变量 实现Black Hole Algorithm的代码 ...
random() * weights_sum for (weight, next_node) in weights: if rand > weight: rand -= weight else: break return next_node def AntColonyRunner(cities, verbose=False, plot=False, label={}, algorithm=AntColonySolver, **kwargs): solver = algorithm(cost_fn=distance, verbose=verbose, **...
2015. An advanced ACO algorithm for feature subset selection. Neurocomputing, 147, 271-279.S. Kashef, H. Nezamabadi-pour, An advanced ACO algorithm for feature subset selection, Neurocomputing, 147 (2015) 271-279.An advanced ACO algorithm for feature subset selection[J] . Shima Kashef,...
NN Das and Anjali Saini, "A Study on Association Rule Mining Using ACO Algorithm for Generating Optimized Result Set", International Journal of Computer Science and Mobile Computing, Vol. 2, Issue. 11, November 2013, pp. 123-128.NN Das, Anjali Saini (November 2013), A Study on ...
#include <iostream> #include <fstream> #include <stdlib.h> #include #include <stdio.h> #include <vector> #include <algorithm> using namespace std; #define Ants 100 //蚂蚁的个数 #define C 31 //城市的数量 #define I 50 //最大迭代次数 #define Alpha 1 //表征信息素重要程度的参数 ...
蚁群算法在求解TSP中取得了较好的效果,但相对于遗传算法等优化方法,其缺少系统的理论指导,特别是参数的设置,通常是根据经验或反复试验来选取合适的参数值。 我翻开Internet一查,这Internet不分年代。长长短短的每页上都写着“参数设置”几个字,我横竖睡不着,仔细看了半夜,才从字缝里看出来,满纸上都写着四个字“...
1 蚁群算法(ant colony algorithm,ACA)起源和发展历程 Marco Dorigo等人在研究新型算法的过程中,发现蚁群在寻找食物时,通过分泌一种称为信息素的生物激素交流觅食信息从而能快速的找到目标,于是在1991年在其博士论文中首次系统地提出一种基于蚂蚁种群的新型智能优化算法“蚂蚁系统(Ant system,简称AS)”,后来,提出者及...
),这次要介绍的蚁群算法(Ant Colony Algorithm)却是一种源于自然现象的算法,也是一种 meta heuristic,即与具体问题关系不大的优化算法,也就是它是一种用来在图中寻找优化路径的机率型技术。Marco Dorigo于1992年在他的博士论文中引入,其灵感来源于蚂蚁在寻找食物过程中发现路径的行为。小小的蚂蚁总是能够找到食物,...
Benhala, B., Ahaitouf, A., Fakhfakh, M., Mechaqrane, A.: New Adaptation of the ACO Algorithm for the Analog Circuits Design Optimization. Int. J. Comput. Sci. Issues 9 (3), 360–367 (2012)New Adaptation of the ACO Algorithm for the Analog Circuits Design Optimization[J] . Ben...