优化后的版本，增加了必要注释，修改了绘图逻辑

Decode.py

@@ -32,7 +32,7 @@ class Decode:
         Site = 0
         # 按照基因的MS部分按工件序号划分
         for S_i in self.J.values():
-            Ms_decompose.append(MS[Site:Site + S_i])
+            Ms_decompose.append(MS[Site:Site + S_i])    #对MS按照每道工序数进行切分
             Site += S_i
         for i in range(len(Ms_decompose)):  # len(Ms_decompose)表示工件数
             JM_i = []

Encode.py

@@ -22,12 +22,12 @@ class Encode:
         self.RS_num = int(0.2 * Pop_size)  # 随机选择初始化
         self.Len_Chromo = 0
         for i in J.values():
-            self.Len_Chromo += i
+            self.Len_Chromo += i    # 遍历字典J的所有值并求和，即工序数之和
 
     # 生成工序准备的部分
     def OS_List(self):
         OS_list = []
-        for k, v in self.J.items():
+        for k, v in self.J.items(): # 遍历字典J的所有键值对，初始化工序矩阵
             OS_add = [k - 1 for j in range(v)]
             OS_list.extend(OS_add)
         return OS_list
@@ -36,7 +36,7 @@ class Encode:
     def CHS_Matrix(self, C_num):
         return np.zeros([C_num, self.Len_Chromo], dtype=int)
 
-    # 定位每个工件的每道工序的位置
+    # 定位每个工件的每道工序的位置，Job第几个工件，Operation第几道工序
     def Site(self, Job, Operation):
         O_num = 0
         for i in range(len(self.J)):
@@ -84,7 +84,7 @@ class Encode:
         OS_list = self.OS_List()
         OS = self.CHS_Matrix(self.LS_num)
         for i in range(self.LS_num):
-            random.shuffle(OS_list)  # 生成工序排序部分
+            random.shuffle(OS_list)  # (随机打乱)生成工序排序部分
             OS[i] = np.array(OS_list)  # 随机打乱后将其赋值给OS的某一行（因为有一个种群，第i则是赋值在OS的第i行，以此生成完整的OS）
             GJ_List = [i_1 for i_1 in range(self.J_num)]  # 生成工件集
             for g in GJ_List:  # 选择第一个工件（注意：不用随机打乱了）

GA.py

@@ -9,18 +9,18 @@ from Instance import *
 
 class GA():
     def __init__(self):
-        self.Pop_size = 400  # 种群数量
+        self.Pop_size = 300  # 种群数量
         self.Pc = 0.8  # 交叉概率
         self.Pm = 0.3  # 变异概率
         self.Pv = 0.5  # 选择何种方式进行交叉的概率阈值
         self.Pw = 0.95  # 选择何种方式进行变异的概率阈值
-        self.Max_Itertions = 100  # 最大迭代次数
+        self.Max_Itertions = 20  # 最大迭代次数
 
     # 适应度
     def fitness(self, CHS, J, Processing_time, M_num, Len):
         Fit = []
         for i in range(len(CHS)):
-            d = Decode(J, Processing_time, M_num)
+            d = Decode(J, Processing_time, M_num)   #实例化一个解码器，传入问题参数
             Fit.append(d.decode(CHS[i], Len))
         return Fit
 
@@ -34,7 +34,7 @@ class GA():
         """
         T_r = [j for j in range(T0)]
         r = random.randint(1, 10)  # 在区间[1,T0]内产生一个整数r
-        random.shuffle(T_r)
+        random.shuffle(T_r)     #直接打乱T_r，原地修改
         R = T_r[0:r]  # 按照随机数r产生r个互不相等的整数
         OS_1 = CHS1[O_num:2 * T0]
         OS_2 = CHS2[O_num:2 * T0]

Instance.py

@@ -5,7 +5,7 @@ M_num：加工机器数
 O_num：加工工序数
 J_num：工件个数
 """
-
+"""
 CKS201 = [[10, 9, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999],
           [9999, 9999, 14, 16, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999],
           [9999, 9999, 9999, 9999, 15, 25, 21, 9999, 9999, 9999, 9999, 9999],
@@ -24,3 +24,63 @@ J = {i: (5 if i < 4 else 8) for i in range(1, 8)}
 M_num = 12
 O_num = 3 * 5 + 4 * 8
 J_num = 7
+"""
+CKS201 = [
+  [5, 9999, 7, 9999, 9999, 9999],   # 工序1: M1=5, M3=7
+  [9999, 6, 9999, 8, 9999, 9999],    # 工序2: M2=6, M4=8
+  [10, 9999, 9999, 9999, 12, 9999],  # 工序3: M1=10, M5=12
+  [9999, 9999, 15, 9999, 9999, 18],  # 工序4: M3=15, M6=18
+  [9999, 9, 9999, 11, 9999, 9999],   # 工序5: M2=9, M4=11
+  [14, 9999, 9999, 9999, 9999, 16]   # 工序6: M1=14, M6=16
+]
+
+CKS301 =[
+  [9999, 4, 9999, 6, 9999, 9999],    # 工序1: M2=4, M4=6
+  [9999, 9999, 5, 9999, 7, 9999],    # 工序2: M3=5, M5=7
+  [9999, 8, 9999, 9999, 9999, 10],   # 工序3: M2=8, M6=10
+  [12, 9999, 9999, 14, 9999, 9999],  # 工序4: M1=12, M4=14
+  [9999, 9999, 16, 9999, 18, 9999],  # 工序5: M3=16, M5=18
+  [9999, 20, 9999, 9999, 9999, 22]   # 工序6: M2=20, M6=22
+]
+
+CKS401 =[
+  [3, 9999, 9999, 9999, 5, 9999],    # 工序1: M1=3, M5=5
+  [9999, 7, 9999, 9999, 9999, 9],    # 工序2: M2=7, M6=9
+  [9999, 9999, 11, 13, 9999, 9999],  # 工序3: M3=11, M4=13
+  [15, 9999, 9999, 9999, 17, 9999],  # 工序4: M1=15, M5=17
+  [9999, 19, 9999, 9999, 9999, 21],  # 工序5: M2=19, M6=21
+  [9999, 9999, 23, 25, 9999, 9999]   # 工序6: M3=23, M4=25
+]
+
+CKS501 =[
+  [9999, 9999, 9999, 6, 9999, 8],    # 工序1: M4=6, M6=8
+  [10, 9999, 12, 9999, 9999, 9999],  # 工序2: M1=10, M3=12
+  [9999, 14, 9999, 9999, 16, 9999],  # 工序3: M2=14, M5=16
+  [9999, 9999, 9999, 18, 9999, 20],  # 工序4: M4=18, M6=20
+  [22, 9999, 24, 9999, 9999, 9999],  # 工序5: M1=22, M3=24
+  [9999, 26, 9999, 9999, 28, 9999]   # 工序6: M2=26, M5=28
+]
+
+CKS601 =[
+  [9999, 9999, 5, 9999, 9999, 7],    # 工序1: M3=5, M6=7
+  [9, 9999, 9999, 11, 9999, 9999],   # 工序2: M1=9, M4=11
+  [9999, 13, 9999, 9999, 15, 9999],  # 工序3: M2=13, M5=15
+  [9999, 9999, 17, 9999, 9999, 19],  # 工序4: M3=17, M6=19
+  [21, 9999, 9999, 23, 9999, 9999],  # 工序5: M1=21, M4=23
+  [9999, 25, 9999, 9999, 27, 9999]   # 工序6: M2=25, M5=27
+]
+
+CKS701 =[
+  [4, 6, 9999, 9999, 9999, 9999],    # 工序1: M1=4, M2=6
+  [9999, 9999, 8, 10, 9999, 9999],   # 工序2: M3=8, M4=10
+  [9999, 9999, 9999, 9999, 12, 14],  # 工序3: M5=12, M6=14
+  [16, 18, 9999, 9999, 9999, 9999],  # 工序4: M1=16, M2=18
+  [9999, 9999, 20, 22, 9999, 9999],  # 工序5: M3=20, M4=22
+  [9999, 9999, 9999, 9999, 24, 26]   # 工序6: M5=24, M6=26
+]
+
+Processing_time = [CKS201] + [CKS301] + [CKS401]+ [CKS501]+ [CKS601]+ [CKS701]
+J = {i: 6 for i in range(1, 7)}
+M_num = 6
+O_num = 6 * 6
+J_num = 6

main.py

@@ -1,5 +1,4 @@
 import random
-
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -13,7 +12,7 @@ from Instance import *
 def Gantt(Machines):
     M = ['red', 'blue', 'yellow', 'orange', 'green', 'palegoldenrod', 'purple', 'pink', 'Thistle', 'Magenta',
          'SlateBlue', 'RoyalBlue', 'Cyan', 'Aqua', 'floralwhite', 'ghostwhite', 'goldenrod', 'mediumslateblue',
-         'navajowhite', 'navy', 'sandybrown', 'moccasin']
+         'navajowhite', 'nawy', 'sandybrown', 'moccasin']
     for i in range(len(Machines)):
         Machine = Machines[i]
         Start_time = Machine.O_start
@@ -49,13 +48,8 @@ if __name__ == '__main__':
         if best_fitness < Optimal_fit:
             Optimal_fit = best_fitness
             Optimal_CHS = Best
-            Best_fit.append(Optimal_fit)
-            print('best_fitness', best_fitness)
-            d = Decode(J, Processing_time, M_num)
-            Fit.append(d.decode(Optimal_CHS, O_num))
-            Gantt(d.Machines)
-        else:
-            Best_fit.append(Optimal_fit)
+            print('iter_{}: new best_fitness = {}'.format(i, best_fitness))
+        Best_fit.append(Optimal_fit)  # 始终记录当前最优适应度
         for j in range(len(C)):
             Cafter = []
             if random.random() < g.Pc:
@@ -76,10 +70,26 @@ if __name__ == '__main__':
             if Cafter != []:
                 Fit = g.fitness(Cafter, J, Processing_time, M_num, O_num)
                 C[j] = Cafter[Fit.index(min(Fit))]
-    x = np.linspace(0, 50, g.Max_Itertions)
+
+    # 迭代结束后输出最终结果
+    print("\n=== 优化结果 ===")
+    print("最优适应度 (最少完成时间):", Optimal_fit)
+
+    # 解码最优解并绘制甘特图
+    d = Decode(J, Processing_time, M_num)
+    final_fitness = d.decode(Optimal_CHS, O_num)
+    print("解码验证适应度:", final_fitness)
+
+    # 绘制最优甘特图
+    Gantt(d.Machines)
+
+    # 绘制收敛曲线
+    x = np.linspace(0, g.Max_Itertions, g.Max_Itertions)
+    plt.figure()
     plt.plot(x, Best_fit, '-k')
-    plt.title('the maximum completion time of each iteration')
+    plt.title('The maximum completion time of each iteration')
     plt.ylabel('Cmax')
-    plt.xlabel('Test Num')
+    plt.xlabel('Iteration')
+    plt.grid(True)
     plt.savefig('最大完成时间的优化过程.png')
-    plt.show()
+    plt.show()