def selection(self):
newSpecies = np.zeros([self.Population, self.GeneLen])
B = self.decoding(self.Species)
# ----------------------------------------------------
# 两种适应度
# ----------------------------------------------------
self.fitness = np.exp(-f2(B[:, 0], B[:, 1]))
# self.fitness = 1 / f2(B[:, 0], B[:, 1])
if self.fitness.max() > self.bestfitness:
self.bestfitness = self.fitness.max()
self.elite = self.Species[self.fitness.argmax()]
# ----------------------------------------------------
# 传说中的联赛选择
# ----------------------------------------------------
# for rank in range(self.Population):
# n1, n2 = np.random.randint(0, self.Population, 2)
# choice = n1 if self.fitness[n1] > self.fitness[n2] else n2
# newSpecies[rank] = self.Species[choice]
# self.Species = newSpecies
# ----------------------------------------------------
# 俄罗斯轮盘赌
# ----------------------------------------------------
prob = self.fitness/self.fitness.sum()
probRank = prob.cumsum()
for rank in range(self.Population):
rand = np.random.random()
for index, item in enumerate(probRank):
if item > rand:
newSpecies[rank] = self.Species[index]
break
self.Species = newSpecies
return self.fitness.mean(), self.fitness.max()
def Calculation(self, swarms):
"""计算适应度"""
fit = f2(swarms[:, 0], swarms[:, 1])
if fit.min() < self.Gfit:
self.Gfit = fit.min()
self.Gcoor = swarms[fit.argmin()]
return fit
avg, pbest = self.selection()
self.crossover()
self.mutation()
avgArray[rank] = avg
pbestArray[rank] = pbest
GbestArray[rank] = self.bestfitness
return avgArray, pbestArray, GbestArray, route
if __name__ == "__main__":
N = 500
ga = GA(128)
avgArray, pbestArray, GbestArray, route = ga.iterations(Iters=N)
B = ga.decoding(ga.elite.reshape(1, ga.GeneLen))
print(B)
print(f2(B[:, 0], B[:, 1]))
plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False
fig = plt.figure()
ax = axisartist.Subplot(fig, 1, 1, 1)
fig.add_axes(ax)
ax.axis[:].set_visible(False)
ax.axis["x"] = ax.new_floating_axis(0, 0.2)
ax.axis["x"].set_axisline_style("->", size=2.0)
ax.axis["y"] = ax.new_floating_axis(1, 0)
ax.axis["y"].set_axisline_style("->", size=2.0)
ax.axis["y"].set_axis_direction('left')
ax.annotate(s='适应度值', xy=(1, 1), xytext=(1, 1))
ax.annotate(s='迭代/次', xy=(N, 0.2), xytext=(N, 0.2))
plt.plot(avgArray, "-.", lw=2)
ax.axis["y"].set_axisline_style("->", size=2.0)
ax.axis["y"].set_axis_direction('left')
ax.annotate(s='适应度值', xy=(1, 1), xytext=(1, 1))
ax.annotate(s='迭代/次', xy=(N, 0), xytext=(N, 0))
plt.plot(avgArray, "-.", lw=2)
plt.plot(pbestArray, ":", lw=2)
plt.plot(GbestArray, "--", lw=2)
plt.legend(["avgArray", "pbestArray", "GbestArray"], loc="upper center")
plt.show()
plt.figure()
delta = 0.1
X1 = np.arange(-4, 4.1, delta)
Y1 = np.arange(-4, 4.1, delta)
X, Y = np.meshgrid(X1, Y1)
Z = f2(X, Y)
"""
二维等高线图figsize=(5, 4), dpi=200
"""
# 20表示绘制的等高线数量=20
contour = plt.contour(X, Y, Z, 20, linewidths=0.5, linestyles="-.")
# 等高线上标明z（即高度）的值，字体大小是10，颜色分别是黑色和红色
plt.clabel(contour, fontsize=4, colors=('k', 'w'))
for i in range(sa.population):
plt.scatter(route[:, i, 0], route[:, i, 1], s=2)
plt.text(route[0, i, 0], route[0, i, 1], "0")
plt.text(route[-1, i, 0], route[-1, i, 1], "-1")
plt.show()