修改蒙特卡洛的输出
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weixin_46229132
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9599215e2e
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61be8ad37c
@ -45,13 +45,15 @@ for iteration in range(num_iterations):
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C = 1
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# 生成随机的行、列分割边界
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horiz = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(R)]
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horiz = [np.clip(np.floor(random.random() * 10) / 10, 0.0, 0.9)
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for _ in range(R)]
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horiz = sorted(set(horiz))
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horiz = horiz if horiz else []
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row_boundaries = [0] + horiz + [1]
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row_boundaries = [boundary * H for boundary in row_boundaries]
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vert = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(C)]
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vert = [np.clip(np.floor(random.random() * 10) / 10, 0.0, 0.9)
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for _ in range(C)]
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vert = sorted(set(vert))
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vert = vert if vert else []
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col_boundaries = [0] + vert + [1]
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@ -108,10 +110,11 @@ for iteration in range(num_iterations):
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# ---------------------------
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# 随机将所有矩形任务分配给 k 个系统（车-机-巢）
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# ---------------------------
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system_tasks = {i: [] for i in range(k)}
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for rect in rectangles:
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system = random.randint(0, k - 1)
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system_tasks[system].append(rect)
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car_paths = [[] for _ in range(k)]
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for i in range(len(row_boundaries) - 1):
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for j in range(len(col_boundaries) - 1):
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car_idx = random.randint(0, k - 1)
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car_paths[car_idx].append(i * (len(col_boundaries) - 1) + j)
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# ---------------------------
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# 对于每个系统，计算该系统的总完成时间 T_k：
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@ -121,18 +124,19 @@ for iteration in range(num_iterations):
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region_center = (H / 2.0, W / 2.0)
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T_k_list = []
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for i in range(k):
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tasks = system_tasks[i]
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tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
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r['center'][1] - region_center[1]))
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total_flight_time = sum(task['flight_time'] for task in tasks)
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if tasks:
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car_path = car_paths[i]
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car_path.sort(key=lambda r: math.dist(
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rectangles[r]['center'], region_center))
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total_flight_time = sum(
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rectangles[point]['flight_time'] for point in car_path)
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if car_path:
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# 车辆从区域中心到第一个任务中心
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car_time = math.dist(tasks[0]['center'],
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car_time = math.dist(rectangles[car_path[0]]['center'],
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region_center) * car_time_factor
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# 依次经过任务中心
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for j in range(len(tasks) - 1):
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prev_center = tasks[j]['center']
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curr_center = tasks[j + 1]['center']
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for j in range(len(car_path) - 1):
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prev_center = rectangles[car_path[j]]['center']
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curr_center = rectangles[car_path[j + 1]]['center']
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car_time += math.dist(curr_center,
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prev_center) * car_time_factor
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# 回到区域中心
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@ -141,7 +145,7 @@ for iteration in range(num_iterations):
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car_time = 0
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# 机巢的计算时间
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total_bs_time = sum(task['bs_time'] for task in tasks)
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total_bs_time = sum(rectangles[point]['bs_time'] for point in car_path)
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T_k = max(total_flight_time + car_time, total_bs_time)
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T_k_list.append(T_k)
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@ -152,7 +156,7 @@ for iteration in range(num_iterations):
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if T_max < best_T:
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best_T = T_max
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best_solution = {
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'system_tasks': system_tasks,
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'car_paths': car_paths,
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'T_k_list': T_k_list,
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'T_max': T_max,
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'iteration': iteration,
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@ -169,40 +173,14 @@ for iteration in range(num_iterations):
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# 输出最佳方案
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# ---------------------------
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if best_solution is not None:
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print("最佳 T (各系统中最长的完成时间):", best_solution['T_max'])
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print(best_solution['iteration'], "次模拟后找到最佳方案:")
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print("分区情况:")
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print("行分段数:", best_solution['R'])
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print("列分段数:", best_solution['C'])
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print("行分割边界:", best_solution['row_boundaries'])
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print("列分割边界:", best_solution['col_boundaries'])
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print("每辆车的运行轨迹情况:")
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car_paths = []
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for i in range(k):
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num_tasks = len(best_solution['system_tasks'][i])
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print(
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f"系统 {i}: 完成时间 T = {best_solution['T_k_list'][i]}, 飞行任务数量: {num_tasks}")
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tasks = best_solution['system_tasks'][i]
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tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
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r['center'][1] - region_center[1]))
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if tasks:
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print(
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f"轨迹路线: 区域中心({region_center[0]:.1f}, {region_center[1]:.1f})", end="")
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current_pos = region_center
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car_path = []
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for j, task in enumerate(tasks, 1):
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current_pos = task['center']
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car_path.append(current_pos)
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print(
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f" -> 任务{j}({current_pos[0]:.1f}, {current_pos[1]:.1f})", end="")
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print(" -> 区域中心")
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car_paths.append(car_path)
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print("最佳 T:", best_solution['T_max'])
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print("最佳路径:", best_solution['car_paths'])
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# 保存分区边界和车辆轨迹到JSON文件
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output_data = {
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'row_boundaries': [boundary / H for boundary in best_solution['row_boundaries']],
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'col_boundaries': [boundary / W for boundary in best_solution['col_boundaries']],
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'car_paths': car_paths
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'car_paths': best_solution['car_paths']
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}
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with open(f'./solutions/mtkl_{params_file}.json', 'w', encoding='utf-8') as f:
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json.dump(output_data, f, ensure_ascii=False, indent=4)
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@ -11,10 +11,12 @@
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],
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"car_paths": [
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[
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0, 2
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0,
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2
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],
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[
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1, 3
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1,
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3
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]
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]
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}
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