增加了所有解的分布图

main.py

@@ -7,7 +7,6 @@ from GA import GA
 from Instance import *
 from NSGA2 import NSGA2
 
-
 # 绘制甘特图
 def Gantt(Machines):
     M = ['red', 'blue', 'yellow', 'orange', 'green', 'palegoldenrod', 'purple', 'pink', 'Thistle', 'Magenta',
@@ -29,7 +28,6 @@ def Gantt(Machines):
     plt.savefig('优化后排程方案的甘特图.png')
     plt.show()
 
-
 if __name__ == '__main__':
     # 初始化参数
     g = GA()
@@ -44,10 +42,18 @@ if __name__ == '__main__':
     Optimal_solutions = []  # 存储非支配解
     Optimal_fit_values = []  # 存储非支配解的目标值
 
+    # 新增：用于存储所有代的所有解
+    all_solutions = []  # 存储所有个体（染色体）
+    all_fitnesses = []  # 存储所有个体的目标值
+
     for i in range(g.Max_Itertions):
         print(f"iter_{i} start!")
         Fit = g.fitness(C, J, Processing_time, M_num, O_num)
 
+        # 记录当前代的所有解
+        all_solutions.extend(C)
+        all_fitnesses.extend(Fit)
+
         # 非支配排序
         rank = nsga2.fast_non_dominated_sort(Fit)
         current_non_dominated = [C[j] for j in range(len(C)) if rank[j] == 0]
@@ -128,15 +134,23 @@ if __name__ == '__main__':
         print(f"\n选中解的目标值: (最大完工时间: {final_fitness[0]}, 负载标准差: {final_fitness[1]:.2f})")
         Gantt(d.Machines)
 
-    # 绘制帕累托前沿
+    # 绘制帕累托前沿（非支配解）
     if Optimal_fit_values:
-        plt.figure()
-        cmax = [fit[0] for fit in Optimal_fit_values]
-        load_std = [fit[1] for fit in Optimal_fit_values]
-        plt.scatter(cmax, load_std, color='red')
-        plt.title('ParetoFront')
-        plt.xlabel('maxtime')
-        plt.ylabel('standard deviation')
+        plt.figure(figsize=(10, 6))
+        cmax_nd = [fit[0] for fit in Optimal_fit_values]
+        load_std_nd = [fit[1] for fit in Optimal_fit_values]
+        plt.scatter(cmax_nd, load_std_nd, color='red', label='Non-dominated solutions', zorder=5)
+
+    # 绘制所有解的点图（所有代的所有个体）
+    if all_fitnesses:
+        cmax_all = [fit[0] for fit in all_fitnesses]
+        load_std_all = [fit[1] for fit in all_fitnesses]
+        plt.scatter(cmax_all, load_std_all, color='blue', alpha=0.5, label='All solutions', zorder=1)
+
+        plt.title('All Solutions and Pareto Front')
+        plt.xlabel('Max Completion Time (Cmax)')
+        plt.ylabel('Load Standard Deviation')
+        plt.legend()
         plt.grid(True)
-        plt.savefig('pareto.png')
+        plt.savefig('all_solutions_pareto.png')
         plt.show()