Python编程实现蚁群算法详解

Python编程实现蚁群算法详解编程实现蚁群算法详解

主要介绍了Python编程实现蚁群算法详解，涉及蚂蚁算法的简介，主要原理及公式，以及Python中的实现代码，具有一定参考价值，需要的朋友可以了解下。

简介简介

蚁群算法(ant colony optimization, ACO)，又称蚂蚁算法，是一种用来在图中寻找优化路径的机率型算法。它由Marco Dorigo于1992年在他的博士论文中提出，其灵感来源于蚂蚁在寻找食物过程中发现

路径的行为。蚁群算法是一种模拟进化算法，初步的研究表明该算法具有许多优良的性质。针对PID控制器参数优化设计问题，将蚁群算法设计的结果与遗传算法设计的结果进行了比较，数值仿真结果

表明，蚁群算法具有一种新的模拟进化优化方法的有效性和应用价值。

定义

各个蚂蚁在没有事先告诉他们食物在什么地方的前提下开始寻找食物。当一只找到食物以后，它会向环境释放一种挥发性分泌物pheromone (称为信息素,该物质随着时间的推移会逐渐挥发消失，信息素

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

height3d = np.array([[2000,1400,800,650,500,750,1000,950,900,800,700,900,1100,1050,1000,1150,1300,1250,1200,1350,1500], [1100,900,700,625,550,825,1100,1150,1200,925,650,750,850,950,1050,1175,1300,1350,1400,1425,1450], [200,400,600,600,600,900,1200,1350,1500,1050,600,600,600,850,1100,1200,1300,1450,1600,1500,1400], [450,500,550,575,600,725,850,875,900,750,600,600,600,725,850,900,950,1150,1350,1400,1450], [700,600,500,550,600,550,500,400,300,450,600,600,600,600,600,600,600,850,1100,1300,1500], [500,525,550,575,600,575,550,450,350,475,600,650,700,650,600,600,600,725,850,1150,1450], [300,450,600,600,600,600,600,500,400,500,600,700,800,700,600,600,600,600,600,1000,1400], [550,525,500,550,600,875,1150,900,650,725,800,700,600,875,1150,1175,1200,975,750,875,1000], [800,600,400,500,600,1150,1700,1300,900,950,1000,700,400,1050,1700,1750,1800,1350,900,750,600], [650,600,550,625,700,1175,1650,1275,900,1100,1300,1275,1250,1475,1700,1525,1350,1200,1050,950,850], [500,600,700,750,800,1200,1600,1250,900,1250,1600,1850,2100,1900,1700,1300,900,1050,1200,1150,1100], [400,375,350,600,850,1200,1550,1250,950,1225,1500,1750,2000,1950,1900,1475,1050,975,900,1175,1450], [300,150,0,450,900,1200,1500,1250,1000,1200,1400,1650,1900,2000,2100,1650,1200,900,600,1200,1800], [600,575,550,750,950,1275,1600,1450,1300,1300,1300,1525,1750,1625,1500,1450,1400,1125,850,1200,1550], [900,1000,1100,1050,1000,1350,1700,1650,1600,1400,1200,1400,1600,1250,900,1250,1600,1350,1100,1200,1300], [750,850,950,900,850,1000,1150,1175,1200,1300,1400,1325,1250,1125,1000,1150,1300,1075,850,975,1100], [600,700,800,750,700,650,600,700,800,1200,1600,1250,900,1000,1100,1050,1000,800,600,750,900], [750,775,800,725,650,700,750,775,800,1000,1200,1025,850,975,1100,950,800,900,1000,1050,1100], [900,850,800,700,600,750,900,850,800,800,800,800,800,950,1100,850,600,1000,1400,1350,1300], [750,800,850,850,850,850,850,825,800,750,700,775,850,1000,1150,875,600,925,1250,1100,950], [600,750,900,1000,1100,950,800,800,800,700,600,750,900,1050,1200,900,600,850,1100,850,600]])

fig = figure()

ax = Axes3D(fig)

X = np.arange(21)

Y = np.arange(21)

X, Y = np.meshgrid(X, Y)

Z = -20*np.exp(-0.2*np.sqrt(np.sqrt(((X-10)**2+(Y-10)**2)/2)))+20+np.e-np.exp((np.cos(2*np.pi*X)+np.sin(2*np.pi*Y))/2)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='cool')

ax.plot([point0[0]],[point0[1]],[point0[2]],'r',marker = u'o',markersize = 15)

ax.plot([point1[0]],[point1[1]],[point1[2]],'r',marker = u'o',markersize = 15)

x0,y0,_ = proj3d.proj_transform(point0[0],point0[1],point0[2], ax.get_proj())

x1,y1,_ = proj3d.proj_transform(point1[0],point1[1],point1[2], ax.get_proj())

label = pylab.annotate(

"start",

xy = (x0, y0), xytext = (-20, 20),

textcoords = 'offset points', ha = 'right', va = 'bottom',

bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 1),

arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'),fontsize=15)

def update_position(e):

x2, y2, _ = proj3d.proj_transform(point0[0],point0[1],point0[2],ax.get_proj())

label.xy = x2,y2

label.update_positions(fig.canvas.renderer)

x1,y1,_ = proj3d.proj_transform(point1[0],point1[1],point1[2],ax.get_proj())

label2.xy = x1,y1

label2.update_positions(fig.canvas.renderer)

fig.canvas.draw()

fig.canvas.mpl_connect('button_release_event', update_position)

基本原理基本原理