import random import matplotlib.pyplot as plt import numpy as np from Decode import Decode from Encode import Encode 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', 'SlateBlue', 'RoyalBlue', 'Cyan', 'Aqua', 'floralwhite', 'ghostwhite', 'goldenrod', 'mediumslateblue', 'navajowhite', 'nawy', 'sandybrown', 'moccasin'] for i in range(len(Machines)): Machine = Machines[i] Start_time = Machine.O_start End_time = Machine.O_end for i_1 in range(len(End_time)): plt.barh(i, width=End_time[i_1] - Start_time[i_1], height=0.8, left=Start_time[i_1], color=M[Machine.assigned_task[i_1][0] - 1], edgecolor='black') plt.text(x=Start_time[i_1] + (End_time[i_1] - Start_time[i_1]) / 2 - 0.5, y=i, s=Machine.assigned_task[i_1][0]) plt.yticks(np.arange(len(Machines) + 1), np.arange(1, len(Machines) + 2)) plt.title('Scheduling Gantt chart') plt.ylabel('Machines') plt.xlabel('Time(min)') 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() e = Encode(Processing_time, g.Pop_size, J, J_num, M_num) CHS1 = e.Global_initial() CHS2 = e.Random_initial() CHS3 = e.Local_initial() C = np.vstack((CHS1, CHS2, CHS3)) # 双目标优化相关 nsga2 = NSGA2(g.Pop_size, 2) # 2个优化目标 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] current_non_dominated_fit = [Fit[j] for j in range(len(C)) if rank[j] == 0] # 更新全局非支配解 Optimal_solutions.extend(current_non_dominated) Optimal_fit_values.extend(current_non_dominated_fit) # 对全局解重新筛选非支配解 if Optimal_solutions: rank_all = nsga2.fast_non_dominated_sort(Optimal_fit_values) # 保留等级为0的解 Optimal_solutions = [Optimal_solutions[j] for j in range(len(Optimal_solutions)) if rank_all[j] == 0] Optimal_fit_values = [Optimal_fit_values[j] for j in range(len(Optimal_fit_values)) if rank_all[j] == 0] # 控制解的数量，过多时保留拥挤度高的解 if len(Optimal_solutions) > g.Pop_size: distance = nsga2.crowding_distance(Optimal_fit_values, rank_all) # 按拥挤度排序并保留前Pop_size个解 sorted_indices = sorted(range(len(distance)), key=lambda k: distance[k], reverse=True) Optimal_solutions = [Optimal_solutions[j] for j in sorted_indices[:g.Pop_size]] Optimal_fit_values = [Optimal_fit_values[j] for j in sorted_indices[:g.Pop_size]] # 选择操作（基于NSGA-II） selected = nsga2.selection(C, Fit) # 交叉变异操作 new_pop = [] for j in range(len(selected)): if random.random() < g.Pc: # 选择另一个个体进行交叉 mate_idx = random.randint(0, len(selected) - 1) if random.random() < g.Pv: offspring1, offspring2 = g.machine_cross(selected[j], selected[mate_idx], O_num) else: offspring1, offspring2 = g.operation_cross(selected[j], selected[mate_idx], O_num, J_num) new_pop.append(offspring1) new_pop.append(offspring2) else: new_pop.append(selected[j]) # 变异操作 if random.random() < g.Pm: if random.random() < g.Pw: mutated = g.machine_variation(selected[j], Processing_time, O_num, J) else: mutated = g.operation_variation(selected[j], O_num, J_num, J, Processing_time, M_num) new_pop.append(mutated) # 保持种群规模 if len(new_pop) > g.Pop_size: # 对新种群进行筛选 new_fit = g.fitness(new_pop, J, Processing_time, M_num, O_num) new_rank = nsga2.fast_non_dominated_sort(new_fit) new_distance = nsga2.crowding_distance(new_fit, new_rank) # 按等级和拥挤度排序 sorted_indices = sorted(range(len(new_pop)), key=lambda k: (new_rank[k], -new_distance[k])) C = [new_pop[j] for j in sorted_indices[:g.Pop_size]] else: C = new_pop # 输出结果 print("\n=== 优化结果 ===") print(f"非支配解数量: {len(Optimal_solutions)}") print("非支配解目标值 (最大完工时间, 负载标准差):") for fit in Optimal_fit_values: print(f"({fit[0]}, {fit[1]:.2f})") # 选择一个折中解绘制甘特图（例如Cmax最小的解） if Optimal_solutions: # 找到Cmax最小的解 cmax_values = [fit[0] for fit in Optimal_fit_values] best_idx = cmax_values.index(min(cmax_values)) Optimal_CHS = Optimal_solutions[best_idx] # 解码并绘图 d = Decode(J, Processing_time, M_num) final_fitness = d.decode(Optimal_CHS, O_num) 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（非支配解） if Optimal_fit_values: 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('all_solutions_pareto.png') plt.show()