diff --git a/ABC.py b/ABC.py
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+++ b/ABC.py
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+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib as mpl
+import copy as copy
+ 
+ 
+
+def initialization(pop,ub,lb,dim):
+    ''' 种群初始化函数'''
+    X = np.zeros([pop,dim]) #声明空间
+    for i in range(pop):
+        for j in range(dim):
+            X[i,j]=(ub[j]-lb[j])*np.random.random()+lb[j] #生成[lb,ub]之间的随机数
+    
+    return X
+     
+def BorderCheck(X,ub,lb,pop,dim):
+    '''边界检查函数'''
+    for i in range(pop):
+        for j in range(dim):
+            if X[i,j]>ub[j]:
+                X[i,j] = ub[j]
+            elif X[i,j]<lb[j]:
+                X[i,j] = lb[j]
+    return X
+
+
+def CaculateFitness(X,fun):
+    '''计算种群的所有个体的适应度值'''
+    pop = X.shape[0]
+    fitness = np.zeros([pop, 1])
+    for i in range(pop):
+        fitness[i] = fun(X[i, :])
+    return fitness
+
+
+def SortFitness(Fit):
+    '''适应度排序'''
+    fitness = np.sort(Fit, axis=0)
+    index = np.argsort(Fit, axis=0)
+    return fitness,index
+
+def SortPosition(X,index):
+    '''根据适应度对位置进行排序'''
+    Xnew = np.zeros(X.shape)
+    for i in range(X.shape[0]):
+        Xnew[i,:] = X[index[i],:]
+    return Xnew
+
+
+def RouletteWheelSelection(P):
+    '''轮盘赌策略'''
+    C = np.cumsum(P)#累加
+    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
+    out = 0
+    #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
+    for i in range(P.shape[0]):
+        if r<C[i]:
+            out = i
+            break
+    return out
+        
+
+def ABC(pop, dim, lb, ub, MaxIter, fun):
+   
+    L = round(0.6*dim*pop) #limit 参数
+    C = np.zeros([pop,1]) #计数器
+    nOnlooker=pop #引领蜂数量
+    
+    X= initialization(pop,ub,lb,dim)  # 初始化种群
+    fitness = CaculateFitness(X, fun)  # 计算适应度值
+    fitness, sortIndex = SortFitness(fitness)  # 对适应度值排序
+    X = SortPosition(X, sortIndex)  # 种群排序
+    GbestScore = copy.copy(fitness[0])#记录最优适应度值
+    GbestPositon = np.zeros([1,dim])
+    GbestPositon[0,:] = copy.copy(X[0, :])#记录最优位置
+    Curve = np.zeros([MaxIter, 1])
+    Xnew = np.zeros([pop,dim])
+    fitnessNew = copy.copy(fitness)
+    for t in range(MaxIter):
+        '''引领蜂搜索'''
+        for i in range(pop):
+            k = np.random.randint(pop)#随机选择一个个体
+            while(k==i):#当k=i时，再次随机选择，直到k不等于i
+                k = np.random.randint(pop)
+            phi = (2*np.random.random([1,dim]) - 1)
+            Xnew[i,:] = X[i,:]+phi*(X[i,:]-X[k,:]) #公式(2.2)位置更新
+        Xnew=BorderCheck(Xnew,ub,lb,pop,dim) #边界检查
+        fitnessNew = CaculateFitness(Xnew, fun)  # 计算适应度值
+        for i in range(pop):
+            if fitnessNew[i]<fitness[i]:#如果适应度值更优，替换原始位置
+                X[i,:]= copy.copy(Xnew[i,:])
+                fitness[i] = copy.copy(fitnessNew[i])
+            else:
+                C[i] = C[i]+1 #如果位置没有更新，累加器+1
+                
+        #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。
+        F = np.zeros([pop,1])
+        MeanCost = np.mean(fitness) #求列表均值
+        for i in range(pop):
+            F[i]=np.exp(-fitness[i]/MeanCost)
+        P=F/sum(F) #式（2.4）
+        '''侦察蜂搜索'''
+        for m in range(nOnlooker):
+            i=RouletteWheelSelection(P)#轮盘赌测量选择个体
+            k = np.random.randint(pop)#随机选择个体
+            while(k==i):
+                k = np.random.randint(pop)
+            phi = (2*np.random.random([1,dim]) - 1)
+            Xnew[i,:] = X[i,:]+phi*(X[i,:]-X[k,:])#位置更新
+        Xnew=BorderCheck(Xnew,ub,lb,pop,dim)#边界检查
+        fitnessNew = CaculateFitness(Xnew, fun)  # 计算适应度值
+        for i in range(pop):
+            if fitnessNew[i]<fitness[i]:#如果适应度值更优，替换原始位置
+                X[i,:]= copy.copy(Xnew[i,:])
+                fitness[i] = copy.copy(fitnessNew[i])
+            else:
+                C[i] = C[i]+1 #如果位置没有更新，累加器+1
+        '''判断limit条件，并进行更新'''
+        for i in range(pop):
+            if C[i]>=L:
+                for j in range(dim):
+                    X[i, j] = np.random.random() * (ub[j] - lb[j]) + lb[j]
+                    C[i] = 0
+                
+        fitness = CaculateFitness(X, fun)  # 计算适应度值
+        fitness, sortIndex = SortFitness(fitness)  # 对适应度值排序
+        X = SortPosition(X, sortIndex)  # 种群排序
+        if fitness[0] <= GbestScore:  # 更新全局最优
+            GbestScore = copy.copy(fitness[0])
+            GbestPositon[0,:] = copy.copy(X[0, :])
+        Curve[t] = GbestScore
+        print(t,MaxIter)
+
+    return GbestScore, GbestPositon, Curve
+
+
+
+'''适应度函数'''
+def fun(x):
+        fitness=0
+        for i in range(3):
+            fitness=fitness+(x[i]**2-10*np.cos(2*np.pi*x[i])+10)
+        return fitness
+
+
+
+
+if __name__=="__main__":
+#设置参数
+    pop = 50 #种群数量
+    MaxIter = 500 #最大迭代次数
+    dim = 3 #维度
+    lb = np.array([-5,-5,-5]) #下边界
+    ub = np.array([5,5,5])#上边界
+    #适应度函数选择
+    fobj = fun
+    GbestScore,GbestPositon,Curve = ABC(pop,dim,lb,ub,MaxIter,fobj) 
+    print('最优适应度值：',GbestScore)
+    print('最优解：',GbestPositon)
+
+    #绘制适应度曲线
+    plt.figure(1)
+    plt.plot(Curve,'r-',linewidth=2)
+    plt.xlabel('Iteration',fontsize='medium')
+    plt.ylabel("Fitness",fontsize='medium')
+    plt.grid()
+    plt.title('ABC',fontsize='large')
+    plt.show()
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