修改蒙特卡洛的输出

mtkl_sovler.py

@@ -45,13 +45,15 @@ for iteration in range(num_iterations):
     C = 1
 
     # 生成随机的行、列分割边界
-    horiz = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(R)]
+    horiz = [np.clip(np.floor(random.random() * 10) / 10, 0.0, 0.9)
+             for _ in range(R)]
     horiz = sorted(set(horiz))
     horiz = horiz if horiz else []
     row_boundaries = [0] + horiz + [1]
     row_boundaries = [boundary * H for boundary in row_boundaries]
 
-    vert = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(C)]
+    vert = [np.clip(np.floor(random.random() * 10) / 10, 0.0, 0.9)
+            for _ in range(C)]
     vert = sorted(set(vert))
     vert = vert if vert else []
     col_boundaries = [0] + vert + [1]
@@ -108,10 +110,11 @@ for iteration in range(num_iterations):
     # ---------------------------
     # 随机将所有矩形任务分配给 k 个系统（车-机-巢）
     # ---------------------------
-    system_tasks = {i: [] for i in range(k)}
+    car_paths = [[] for _ in range(k)]
-    for rect in rectangles:
+    for i in range(len(row_boundaries) - 1):
-        system = random.randint(0, k - 1)
+        for j in range(len(col_boundaries) - 1):
-        system_tasks[system].append(rect)
+            car_idx = random.randint(0, k - 1)
+            car_paths[car_idx].append(i * (len(col_boundaries) - 1) + j)
 
     # ---------------------------
     # 对于每个系统，计算该系统的总完成时间 T_k：
@@ -121,18 +124,19 @@ for iteration in range(num_iterations):
     region_center = (H / 2.0, W / 2.0)
     T_k_list = []
     for i in range(k):
-        tasks = system_tasks[i]
+        car_path = car_paths[i]
-        tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
+        car_path.sort(key=lambda r: math.dist(
-                                            r['center'][1] - region_center[1]))
+            rectangles[r]['center'], region_center))
-        total_flight_time = sum(task['flight_time'] for task in tasks)
+        total_flight_time = sum(
-        if tasks:
+            rectangles[point]['flight_time'] for point in car_path)
+        if car_path:
             # 车辆从区域中心到第一个任务中心
-            car_time = math.dist(tasks[0]['center'],
+            car_time = math.dist(rectangles[car_path[0]]['center'],
                                  region_center) * car_time_factor
             # 依次经过任务中心
-            for j in range(len(tasks) - 1):
+            for j in range(len(car_path) - 1):
-                prev_center = tasks[j]['center']
+                prev_center = rectangles[car_path[j]]['center']
-                curr_center = tasks[j + 1]['center']
+                curr_center = rectangles[car_path[j + 1]]['center']
                 car_time += math.dist(curr_center,
                                       prev_center) * car_time_factor
             # 回到区域中心
@@ -141,7 +145,7 @@ for iteration in range(num_iterations):
             car_time = 0
 
         # 机巢的计算时间
-        total_bs_time = sum(task['bs_time'] for task in tasks)
+        total_bs_time = sum(rectangles[point]['bs_time'] for point in car_path)
         T_k = max(total_flight_time + car_time, total_bs_time)
         T_k_list.append(T_k)
 
@@ -152,7 +156,7 @@ for iteration in range(num_iterations):
     if T_max < best_T:
         best_T = T_max
         best_solution = {
-            'system_tasks': system_tasks,
+            'car_paths': car_paths,
             'T_k_list': T_k_list,
             'T_max': T_max,
             'iteration': iteration,
@@ -169,40 +173,14 @@ for iteration in range(num_iterations):
 # 输出最佳方案
 # ---------------------------
 if best_solution is not None:
-    print("最佳 T (各系统中最长的完成时间):", best_solution['T_max'])
+    print("最佳 T:", best_solution['T_max'])
-    print(best_solution['iteration'], "次模拟后找到最佳方案:")
+    print("最佳路径:", best_solution['car_paths'])
-    print("分区情况:")
-    print("行分段数:", best_solution['R'])
-    print("列分段数:", best_solution['C'])
-    print("行分割边界:", best_solution['row_boundaries'])
-    print("列分割边界:", best_solution['col_boundaries'])
-    print("每辆车的运行轨迹情况:")
-    car_paths = []
-    for i in range(k):
-        num_tasks = len(best_solution['system_tasks'][i])
-        print(
-            f"系统 {i}: 完成时间 T = {best_solution['T_k_list'][i]}, 飞行任务数量: {num_tasks}")
-        tasks = best_solution['system_tasks'][i]
-        tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
-                                            r['center'][1] - region_center[1]))
-        if tasks:
-            print(
-                f"轨迹路线: 区域中心({region_center[0]:.1f}, {region_center[1]:.1f})", end="")
-            current_pos = region_center
-            car_path = []
-            for j, task in enumerate(tasks, 1):
-                current_pos = task['center']
-                car_path.append(current_pos)
-                print(
-                    f" -> 任务{j}({current_pos[0]:.1f}, {current_pos[1]:.1f})", end="")
-            print(" -> 区域中心")
-            car_paths.append(car_path)
 
     # 保存分区边界和车辆轨迹到JSON文件
     output_data = {
         'row_boundaries': [boundary / H for boundary in best_solution['row_boundaries']],
         'col_boundaries': [boundary / W for boundary in best_solution['col_boundaries']],
-        'car_paths': car_paths
+        'car_paths': best_solution['car_paths']
     }
     with open(f'./solutions/mtkl_{params_file}.json', 'w', encoding='utf-8') as f:
         json.dump(output_data, f, ensure_ascii=False, indent=4)

solutions/mtkl_params3.json

@@ -11,10 +11,12 @@
     ],
     "car_paths": [
         [
-            0, 2
+            0,
+            2
         ],
         [
-            1, 3
+            1,
+            3
         ]
     ]
 }