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antMain.py
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import random
import copy
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
def functionG2D(G):
l = len(G[0])
D = np.zeros((l, l, l, l))
for i in range(l):
for j in range(l):
if(G[i][j] == 0):
for m in range(l):
for n in range(l):
if(G[m][n] == 0):
im = abs(i - m)
jn = abs(j - n)
if im + jn == 1 or (im == 1 and jn == 1):
D[i][j][m][n] = np.sqrt(im + jn)
return D
def main():
# figure1: 绘制最优路径更新情况
# figure2: 绘制蚂蚁最优路径
# figure3: 绘制每次迭代中的蚂蚁最优路径
figure1 = 1
figure2 = 1
figure3 = 1
# G 地形图为01矩阵，1表示障碍物
G = [[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],]
G = list(zip(*G[::-1]))
MM = len(G[0])
# Tau 初始化信息素矩阵
Tau = np.ones((MM, MM, MM, MM))
Tau = 8.0 * Tau
# 迭代次数（蚂蚁出动多少波）
K = 100
# 蚂蚁个数
M = 50
# 最短路径起始点
S = [0, MM-1]
# 最短路径目的点
E = [MM-1, 0]
# Alpha 表征信息素重要程度的参数
Alpha = 1
# Beta 表征启发式因子重要程度的参数
Beta = 7
# Rho 信息素蒸发系数
Rho = 0.3
# Q 信息素增加强度系数
Q = 1
mink = 0
minl = 0
minkl = float("inf")
D = functionG2D(G)
# N 问题的规模（像素个数）
N = len(D[0])
# 小方格像素的边长
a = 1
# 起始点横纵坐标
Sx = S[0] + 0.5
Sy = S[1] + 0.5
# 终止点横坐标
Ex = E[0] + 0.5
# 终止点纵坐标
Ey = E[1] + 0.5
# 启发式信息，取为至目标点的直线距离的倒数
Eta = np.zeros((MM, MM))
# 以下启发式信息矩阵
for i in range(MM):
for j in range(MM):
ix = i + 0.5
iy = j + 0.5
if ix != Ex or iy != Ey:
Eta[i][j] = 1.0 / np.sqrt(np.square(ix - Ex) + np.square(iy - Ey))
else:
Eta[i][j] = 100
ROUTES = []
for i in range(K):
tmp = []
for j in range(M):
tmp.append([])
ROUTES.append(tmp)
PL = np.zeros((K, M))
for k in range(K):
print("round:" + str(k))
for m in range(M):
W = S
Path = [S]
PLkm = 0
TABUkm = np.ones((MM, MM))
TABUkm[S[0]][S[1]] = 0
DD = copy.deepcopy(D)
#下一步可以前往的结点
DW = DD[W[0]][W[1]]
DW1 = np.nonzero(DW)
for j in range(len(DW1[0])):
if TABUkm[DW1[0][j]][DW1[1][j]] == 0:
DW[DW1[0][j]][DW1[1][j]] = 0
LJD = np.nonzero(DW)
LenLJD = len(LJD[0])
# 蚂蚁遇到食物或者陷入死胡同则觅食停止
while W != E and LenLJD >= 1:
PP = np.zeros(LenLJD)
for i in range(LenLJD):
PP[i] = (Tau[W[0]][W[1]][LJD[0][i]][LJD[1][i]] ** Alpha) * (Eta[LJD[0][i]][LJD[1][i]] ** Beta)
sumpp = PP.sum()
PP = PP / sumpp
Pcum = np.zeros(LenLJD)
Pcum[0] = PP[0]
for i in range(1, LenLJD):
Pcum[i] = Pcum[i-1] + PP[i]
Select = -1
p = random.random()
for i in range(len(Pcum)):
if Pcum[i] >= p:
Select = i
break
to_visit = [LJD[0][Select], LJD[1][Select]]
Path.append(to_visit)
PLkm += DD[W[0]][W[1]][to_visit[0]][to_visit[1]]
W = to_visit
for kk in range(N):
for kkk in range(N):
if TABUkm[kk][kkk] == 0:
DD[W[0]][W[1]][kk][kkk] = 0
DD[kk][kkk][W[0]][W[1]] = 0
TABUkm[W[0]][W[1]] = 0
DW = DD[W[0]][W[1]]
DW1 = np.nonzero(DW)
for j in range(len(DW1[0])):
if TABUkm[DW1[0][j]][DW1[1][j]] == 0:
DW[DW1[0][j]][DW1[1][j]] = 0
LJD = np.nonzero(DW)
LenLJD = len(LJD[0])
ROUTES[k][m] = Path
if Path[-1] == E:
PL[k][m] = PLkm
if PLkm < minkl:
mink = k
minl = m
minkl = PLkm
else:
PL[k][m] = 0
Delta_Tau = np.zeros((N, N, N, N))
for m in range(M):
if PL[k][m]:
ROUT = ROUTES[k][m]
TS = len(ROUT) - 1
PL_km = PL[k][m]
for s in range(TS):
x = ROUT[s]
y = ROUT[s + 1]
Delta_Tau[x[0]][x[1]][y[0]][y[1]] = Delta_Tau[x[0]][x[1]][y[0]][y[1]] + Q / PL_km
Delta_Tau[y[0]][y[1]][x[0]][x[1]] = Delta_Tau[y[0]][y[1]][x[0]][x[1]] + Q / PL_km
Tau = (1.0 - Rho) * Tau + Delta_Tau
if figure1 == 1:
minPL = []
for i in range(K):
PLK = PL[i]
notZero = np.nonzero(PLK)
if len(notZero) == 0:
continue
PLKPLK = []
for j in range(len(notZero[0])):
PLKPLK.append(PLK[notZero[0][j]])
if len(PLKPLK) == 0:
continue
minPL.append(min(PLKPLK))
plt.figure(figsize=(5, 5))
plt.grid(True)
# x_major_locator = MultipleLocator(1)
# y_major_locator = MultipleLocator(1)
# ax = plt.gca()
# ax.xaxis.set_major_locator(x_major_locator)
# ax.yaxis.set_major_locator(y_major_locator)
plt.plot(minPL)
# 绘制蚂蚁爬行路径
if figure2 == 1:
plt.figure(figsize=(5, 5))
plt.grid(True)
x_major_locator = MultipleLocator(1)
y_major_locator = MultipleLocator(1)
ax = plt.gca()
ax.xaxis.set_major_locator(x_major_locator)
ax.yaxis.set_major_locator(y_major_locator)
plt.xlim(0, 20)
plt.ylim(0, 20)
for i in range(MM):
for j in range(MM):
x1 = i
y1 = j
x2 = i
y2 = j + 1
x3 = i + 1
y3 = j + 1
x4 = i + 1
y4 = j
if G[i][j] == 1:
plt.fill([x1, x2, x3, x4], [y1, y2, y3, y4], "gray")
else:
plt.fill([x1, x2, x3, x4], [y1, y2, y3, y4], "white")
ROUT = ROUTES[mink][minl]
LENROUT = len(ROUT)
Rx = []
Ry = []
for ii in range(LENROUT):
Rx.append(ROUT[ii][0] + 0.5)
Ry.append(ROUT[ii][1] + 0.5)
plt.plot(Rx, Ry)
# 绘制各代蚂蚁的爬行图
if figure3 == 1:
plt.figure(figsize=(5, 5))
plt.grid(True)
x_major_locator = MultipleLocator(1)
y_major_locator = MultipleLocator(1)
ax = plt.gca()
ax.xaxis.set_major_locator(x_major_locator)
ax.yaxis.set_major_locator(y_major_locator)
plt.xlim(0, 20)
plt.ylim(0, 20)
for i in range(MM):
for j in range(MM):
x1 = i
y1 = j
x2 = i
y2 = j + 1
x3 = i + 1
y3 = j + 1
x4 = i + 1
y4 = j
if G[i][j] == 1:
plt.fill([x1, x2, x3, x4], [y1, y2, y3, y4], "gray")
else:
plt.fill([x1, x2, x3, x4], [y1, y2, y3, y4], "white")
for k in range(K):
PLK = PL[k]
choose = 0
notZero = np.nonzero(PLK)
if len(notZero[0]) != 0:
minklTmp = float('inf')
for kk in notZero[0]:
if PLK[kk] < minklTmp:
minklTmp = PLK[kk]
choose = kk
else:
continue
ROUT = ROUTES[k][choose]
LENROUT = len(ROUT)
Rx = []
Ry = []
for ii in range(LENROUT):
Rx.append(ROUT[ii][0] + 0.5)
Ry.append(ROUT[ii][1] + 0.5)
plt.plot(Rx, Ry)
if figure1 + figure2 + figure3 >= 1:
plt.show()
main()