archieve: homework5
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# 默认忽略的文件
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/shelf/
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/workspace.xml
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# 基于编辑器的 HTTP 客户端请求
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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<?xml version="1.0" encoding="UTF-8"?>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="ai-homework" />
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/homework5.iml" filepath="$PROJECT_DIR$/.idea/homework5.iml" />
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</modules>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
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</project>
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import matplotlib
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import numpy as np
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from numpy.ma import cos
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import matplotlib.pyplot as plt
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from matplotlib import cm
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from mpl_toolkits.mplot3d import Axes3D
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import datetime
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matplotlib.use('Qt5Agg')
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DNA_SIZE = 24 # 编码长度
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POP_SIZE = 100 # 种群大小
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CROSS_RATE = 0.8 # 交叉率
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MUTA_RATE = 0.15 # 变异率
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Iterations = 1000 # 代次数
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X_BOUND = [0, 10] # X区间
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Y_BOUND = [0, 10] # Y区间
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def F(x, y): # 适应度函数
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return (6.452 * (x + 0.125 * y) * (cos(x) - cos(2 * y)) ** 2) / (
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0.8 + (x - 4.2) ** 2 + 2 * (y - 7) ** 2) + 3.226 * y
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def decodeDNA(pop): # 解码
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x_pop = pop[:, 1::2] # 奇数列表示X
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y_pop = pop[:, ::2] # 偶数列表示y
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x = x_pop.dot(2 ** np.arange(DNA_SIZE)[::-1]) / float(2 ** DNA_SIZE - 1) * (X_BOUND[1] - X_BOUND[0]) + X_BOUND[0]
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y = y_pop.dot(2 ** np.arange(DNA_SIZE)[::-1]) / float(2 ** DNA_SIZE - 1) * (Y_BOUND[1] - Y_BOUND[0]) + Y_BOUND[0]
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return x, y
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def getfitness(pop):
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x, y = decodeDNA(pop)
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temp = F(x, y)
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return (temp - np.min(temp)) + 0.0001 # 减去最小的适应度是为了防止适应度出现负数
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def select(pop, fitness): # 根据适应度选择
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temp = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True, p=(fitness) / (fitness.sum()))
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return pop[temp]
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def crossmuta(pop, CROSS_RATE):
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new_pop = []
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for i in pop: # 遍历种群中的每一个个体，将该个体作为父代
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temp = i # 子代先得到父亲的全部基因
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if np.random.rand() < CROSS_RATE: # 以交叉概率发生交叉
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j = pop[np.random.randint(POP_SIZE)] # 从种群中随机选择另一个个体，并将该个体作为母代
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cpoints1 = np.random.randint(0, DNA_SIZE * 2 - 1) # 随机产生交叉的点
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cpoints2 = np.random.randint(cpoints1, DNA_SIZE * 2)
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temp[cpoints1:cpoints2] = j[cpoints1:cpoints2] # 子代得到位于交叉点后的母代的基因
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mutation(temp, MUTA_RATE) # 后代以变异率发生变异
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new_pop.append(temp)
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return new_pop
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def mutation(temp, MUTA_RATE):
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if np.random.rand() < MUTA_RATE: # 以MUTA_RATE的概率进行变异
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mutate_point = np.random.randint(0, DNA_SIZE) # 随机产生一个实数，代表要变异基因的位置
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temp[mutate_point] = temp[mutate_point] ^ 1 # 将变异点的二进制为反转
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def print_info(pop): # 用于输出结果
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fitness = getfitness(pop)
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maxfitness = np.argmax(fitness) # 返回最大值的索引值
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print("max_fitness:", fitness[maxfitness])
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x, y = decodeDNA(pop)
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print("最优的基因型：", pop[maxfitness])
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print("(x, y):", (x[maxfitness], y[maxfitness]))
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print("F(x,y)_max = ", F(x[maxfitness], y[maxfitness]))
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def plot_3d(ax):
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X = np.linspace(*X_BOUND, 100)
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Y = np.linspace(*Y_BOUND, 100)
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X, Y = np.meshgrid(X, Y)
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Z = F(X, Y)
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ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm)
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ax.set_zlim(-20, 100)
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_zlabel('z')
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plt.pause(0.01) # 缩短暂停时间
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# 移除 plt.show()
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start_t = datetime.datetime.now()
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if __name__ == "__main__":
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plt.ion() # 提前设置交互模式
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d') # 修改3D坐标创建方式
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plot_3d(ax)
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pop = np.random.randint(2, size=(POP_SIZE, DNA_SIZE * 2))
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for _ in range(Iterations):
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x, y = decodeDNA(pop)
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if 'sca' in locals():
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sca.remove()
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sca = ax.scatter(x, y, F(x, y), c='red', marker='o', s=50) # 增大标记尺寸
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plt.draw() # 强制重绘
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plt.pause(0.01) # 保证足够的更新时间
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pop = np.array(crossmuta(pop, CROSS_RATE))
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fitness = getfitness(pop)
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pop = select(pop, fitness) # 选择生成新的种群
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end_t = datetime.datetime.now()
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print((end_t - start_t).seconds)
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print_info(pop)
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plt.ioff()
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plot_3d(ax)
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# 默认忽略的文件
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/shelf/
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||||||
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/workspace.xml
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||||||
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# 基于编辑器的 HTTP 客户端请求
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||||||
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/httpRequests/
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||||||
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# Datasource local storage ignored files
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||||||
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/dataSources/
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||||||
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/dataSources.local.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="com.codeverse.userSettings.MarscodeWorkspaceAppSettingsState">
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<option name="ckgOperationStatus" value="SUCCESS" />
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</component>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="jdk" jdkName="ai-homework" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="ai-homework" />
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/homework6.iml" filepath="$PROJECT_DIR$/.idea/homework6.iml" />
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</modules>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
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</project>
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import numpy as np
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import random
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import matplotlib.pyplot as plt
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import copy
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# 各个城市的坐标
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# City_Map = [[106.54,29.59]
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# ,[91.11,29.97]
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# ,[87.68,43.77]
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# ,[106.27,38.47]
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# ,[111.65,40.82]
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# ,[108.33,22.84]
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# ,[126.63,45.75]
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# ,[125.35,43.88]
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# ,[123.38,41.8]
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# ,[114.48,38.03]
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# ,[112.53,37.87]
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# ,[101.74,36.56]
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# ,[117,36.65]
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# ,[113.6,34.76]
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# ,[118.78,32.04]
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# ,[117.27,31.86]]
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City_Map = 100 * np.random.rand(20, 2) # 随机产生20个城市
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DNA_SIZE = len(City_Map) # 编码长度
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POP_SIZE = 200 # 种群大小
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CROSS_RATE = 0.6 # 交叉率
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MUTA_RATE = 0.2 # 变异率
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Iterations = 1000 # 迭代次数
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def distance(DNA): # 根据DNA的路线计算距离
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dis = 0
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temp = City_Map[DNA[0]]
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for i in DNA[1:]:
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dis = dis + ((City_Map[i][0] - temp[0]) ** 2 + (City_Map[i][1] - temp[1]) ** 2) ** 0.5
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temp = City_Map[i]
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return dis + ((temp[0] - City_Map[DNA[0]][0]) ** 2 + (temp[1] - City_Map[DNA[0]][1]) ** 2) ** 0.5
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def getfitness(pop): # 计算种群适应度，这里适应度用距离的倒数表示
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temp = []
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for i in range(len(pop)):
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temp.append(1 / (distance(pop[i])))
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return temp - np.min(temp)
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def select(pop, fitness): # 根据适应度选择，以赌轮盘的形式，适应度越大的个体被选中的概率越大
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s = fitness.sum()
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temp = np.random.choice(np.arange(len(pop)), size=POP_SIZE, replace=True, p=(fitness / s))
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p = []
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for i in temp:
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p.append(pop[i])
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return p
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def mutation(DNA, MUTA_RATE): # 进行变异
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if np.random.rand() < MUTA_RATE: # 以MUTA_RATE的概率进行变异
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mutate_point1 = np.random.randint(0, DNA_SIZE) # 随机产生一个实数，代表要变异基因的位置
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mutate_point2 = np.random.randint(0, DNA_SIZE) # 随机产生一个实数，代表要变异基因的位置
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while (mutate_point1 == mutate_point2): # 保证2个所选位置不相等
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mutate_point2 = np.random.randint(0, DNA_SIZE)
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DNA[mutate_point1], DNA[mutate_point2] = DNA[mutate_point2], DNA[mutate_point1] # 2个所选位置进行互换
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def crossmuta(pop, CROSS_RATE): # 交叉变异
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new_pop = []
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for i in range(len(pop)): # 遍历种群中的每一个个体，将该个体作为父代
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n = np.random.rand()
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if n >= CROSS_RATE: # 大于交叉概率时不发生变异，该子代直接进入下一代
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temp = pop[i].copy()
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new_pop.append(temp)
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if n < CROSS_RATE: # 小于交叉概率时发生变异
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list1 = pop[i].copy()
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list2 = pop[np.random.randint(POP_SIZE)].copy() # 选取种群中另一个个体进行交叉
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status = True
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while status: # 产生2个不相等的节点，中间部分作为交叉段，采用部分匹配交叉
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k1 = random.randint(0, len(list1) - 1)
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k2 = random.randint(0, len(list2) - 1)
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if k1 < k2:
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status = False
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k11 = k1
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fragment1 = list1[k1: k2]
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fragment2 = list2[k1: k2]
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list1[k1: k2] = fragment2
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list2[k1: k2] = fragment1
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del list1[k1: k2]
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left1 = list1
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offspring1 = []
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for pos in left1:
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if pos in fragment2:
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pos = fragment1[fragment2.index(pos)]
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while pos in fragment2:
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pos = fragment1[fragment2.index(pos)]
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offspring1.append(pos)
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continue
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offspring1.append(pos)
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for i in range(0, len(fragment2)):
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offspring1.insert(k11, fragment2[i])
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k11 += 1
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temp = offspring1.copy()
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|
mutation(temp, MUTA_RATE)
|
||||||
|
|
||||||
|
new_pop.append(temp) # 把部分匹配交叉后形成的合法个体加入到下一代种群
|
||||||
|
|
||||||
|
return new_pop
|
||||||
|
|
||||||
|
|
||||||
|
def print_info(pop): # 用于输出结果
|
||||||
|
fitness = getfitness(pop)
|
||||||
|
maxfitness = np.argmax(fitness) # 得到种群中最大适应度个体的索引
|
||||||
|
# 打印结果
|
||||||
|
print("最优的基因型：", pop[maxfitness])
|
||||||
|
print("最短距离：", distance(pop[maxfitness]))
|
||||||
|
# 按最优结果顺序把地图上的点加入到best_map列表中
|
||||||
|
best_map = []
|
||||||
|
for i in pop[maxfitness]:
|
||||||
|
best_map.append(City_Map[i])
|
||||||
|
best_map.append(City_Map[pop[maxfitness][0]])
|
||||||
|
X = np.array((best_map))[:, 0]
|
||||||
|
Y = np.array((best_map))[:, 1]
|
||||||
|
# 绘制地图以及路线
|
||||||
|
plt.figure()
|
||||||
|
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||||||
|
plt.scatter(X, Y)
|
||||||
|
for dot in range(len(X) - 1):
|
||||||
|
plt.annotate(pop[maxfitness][dot], xy=(X[dot], Y[dot]), xytext=(X[dot], Y[dot]))
|
||||||
|
plt.annotate('start', xy=(X[0], Y[0]), xytext=(X[0] + 1, Y[0]))
|
||||||
|
plt.plot(X, Y)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__": # 主循环
|
||||||
|
# 生成初代种群pop
|
||||||
|
pop = []
|
||||||
|
list = list(range(DNA_SIZE))
|
||||||
|
for i in range(POP_SIZE):
|
||||||
|
random.shuffle(list)
|
||||||
|
l = list.copy()
|
||||||
|
pop.append(l)
|
||||||
|
best_dis = []
|
||||||
|
# 进行选择，交叉，变异，并把每代的最优个体保存在best_dis中
|
||||||
|
for i in range(Iterations): # 迭代N代
|
||||||
|
pop = crossmuta(pop, CROSS_RATE)
|
||||||
|
fitness = getfitness(pop)
|
||||||
|
maxfitness = np.argmax(fitness)
|
||||||
|
best_dis.append(distance(pop[maxfitness]))
|
||||||
|
pop = select(pop, fitness) # 选择生成新的种群
|
||||||
|
|
||||||
|
print_info(pop) # 打印信息
|
||||||
|
|
||||||
|
print('逐代的最小距离：', best_dis)
|
||||||
|
|
||||||
|
# 画图
|
||||||
|
plt.figure()
|
||||||
|
plt.plot(range(Iterations), best_dis)
|
||||||
|
plt.show()
|
||||||
|
plt.close()
|
||||||
|
|
||||||
Binary file not shown.
Reference in New Issue
Block a user