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import numpy as np
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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 matplotlib as mpl
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import copy as copy
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def initialization(pop,ub,lb,dim):
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''' 种群初始化函数'''
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X = np.zeros([pop,dim]) #声明空间
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for i in range(pop):
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for j in range(dim):
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X[i,j]=(ub[j]-lb[j])*np.random.random()+lb[j] #生成[lb,ub]之间的随机数
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return X
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def BorderCheck(X,ub,lb,pop,dim):
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'''边界检查函数'''
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for i in range(pop):
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for j in range(dim):
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if X[i,j]>ub[j]:
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X[i,j] = ub[j]
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elif X[i,j]<lb[j]:
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X[i,j] = lb[j]
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return X
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def CaculateFitness(X,fun):
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'''计算种群的所有个体的适应度值'''
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pop = X.shape[0]
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fitness = np.zeros([pop, 1])
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for i in range(pop):
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fitness[i] = fun(X[i, :])
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return fitness
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def SortFitness(Fit):
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'''适应度排序'''
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fitness = np.sort(Fit, axis=0)
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index = np.argsort(Fit, axis=0)
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return fitness,index
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def SortPosition(X,index):
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'''根据适应度对位置进行排序'''
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Xnew = np.zeros(X.shape)
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for i in range(X.shape[0]):
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Xnew[i,:] = X[index[i],:]
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return Xnew
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def RouletteWheelSelection(P):
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'''轮盘赌策略'''
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C = np.cumsum(P)#累加
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r = np.random.random()*C[-1]#定义选择阈值，将随机概率与总和的乘积作为阈值
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out = 0
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#若大于或等于阈值，则输出当前索引，并将其作为结果，循环结束
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for i in range(P.shape[0]):
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if r<C[i]:
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out = i
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break
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return out
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def ABC(pop, dim, lb, ub, MaxIter, fun):
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L = round(0.6*dim*pop) #limit 参数
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C = np.zeros([pop,1]) #计数器
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nOnlooker=pop #引领蜂数量
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X= initialization(pop,ub,lb,dim) # 初始化种群
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fitness = CaculateFitness(X, fun) # 计算适应度值
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fitness, sortIndex = SortFitness(fitness) # 对适应度值排序
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X = SortPosition(X, sortIndex) # 种群排序
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GbestScore = copy.copy(fitness[0])#记录最优适应度值
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GbestPositon = np.zeros([1,dim])
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GbestPositon[0,:] = copy.copy(X[0, :])#记录最优位置
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Curve = np.zeros([MaxIter, 1])
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Xnew = np.zeros([pop,dim])
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fitnessNew = copy.copy(fitness)
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for t in range(MaxIter):
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'''引领蜂搜索'''
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for i in range(pop):
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k = np.random.randint(pop)#随机选择一个个体
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while(k==i):#当k=i时，再次随机选择，直到k不等于i
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k = np.random.randint(pop)
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phi = (2*np.random.random([1,dim]) - 1)
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Xnew[i,:] = X[i,:]+phi*(X[i,:]-X[k,:]) #公式(2.2)位置更新
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Xnew=BorderCheck(Xnew,ub,lb,pop,dim) #边界检查
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fitnessNew = CaculateFitness(Xnew, fun) # 计算适应度值
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for i in range(pop):
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if fitnessNew[i]<fitness[i]:#如果适应度值更优，替换原始位置
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X[i,:]= copy.copy(Xnew[i,:])
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fitness[i] = copy.copy(fitnessNew[i])
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else:
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C[i] = C[i]+1 #如果位置没有更新，累加器+1
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#计算选择适应度权重,这里用的是e的负指数次幂，我的理解是将适应度缩小，减少计算量。
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F = np.zeros([pop,1])
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MeanCost = np.mean(fitness) #求列表均值
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for i in range(pop):
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F[i]=np.exp(-fitness[i]/MeanCost)
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P=F/sum(F) #式（2.4）
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'''侦察蜂搜索'''
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for m in range(nOnlooker):
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i=RouletteWheelSelection(P)#轮盘赌测量选择个体
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k = np.random.randint(pop)#随机选择个体
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while(k==i):
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k = np.random.randint(pop)
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phi = (2*np.random.random([1,dim]) - 1)
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Xnew[i,:] = X[i,:]+phi*(X[i,:]-X[k,:])#位置更新
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Xnew=BorderCheck(Xnew,ub,lb,pop,dim)#边界检查
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fitnessNew = CaculateFitness(Xnew, fun) # 计算适应度值
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for i in range(pop):
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if fitnessNew[i]<fitness[i]:#如果适应度值更优，替换原始位置
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X[i,:]= copy.copy(Xnew[i,:])
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fitness[i] = copy.copy(fitnessNew[i])
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else:
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C[i] = C[i]+1 #如果位置没有更新，累加器+1
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'''判断limit条件，并进行更新'''
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for i in range(pop):
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if C[i]>=L:
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for j in range(dim):
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X[i, j] = np.random.random() * (ub[j] - lb[j]) + lb[j]
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C[i] = 0
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fitness = CaculateFitness(X, fun) # 计算适应度值
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fitness, sortIndex = SortFitness(fitness) # 对适应度值排序
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X = SortPosition(X, sortIndex) # 种群排序
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if fitness[0] <= GbestScore: # 更新全局最优
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GbestScore = copy.copy(fitness[0])
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GbestPositon[0,:] = copy.copy(X[0, :])
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Curve[t] = GbestScore
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print(t,MaxIter)
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return GbestScore, GbestPositon, Curve
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'''适应度函数'''
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def fun(x):
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fitness=0
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for i in range(3):
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fitness=fitness+(x[i]**2-10*np.cos(2*np.pi*x[i])+10)
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return fitness
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if __name__=="__main__":
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#设置参数
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pop = 50 #种群数量
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MaxIter = 500 #最大迭代次数
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dim = 3 #维度
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lb = np.array([-5,-5,-5]) #下边界
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ub = np.array([5,5,5])#上边界
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#适应度函数选择
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fobj = fun
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GbestScore,GbestPositon,Curve = ABC(pop,dim,lb,ub,MaxIter,fobj)
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print('最优适应度值：',GbestScore)
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print('最优解：',GbestPositon)
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#绘制适应度曲线
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plt.figure(1)
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plt.plot(Curve,'r-',linewidth=2)
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plt.xlabel('Iteration',fontsize='medium')
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plt.ylabel("Fitness",fontsize='medium')
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plt.grid()
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plt.title('ABC',fontsize='large')
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plt.show() |
