diff --git a/GA/ga.py b/GA/ga.py index e000ade..976c5ee 100644 --- a/GA/ga.py +++ b/GA/ga.py @@ -4,8 +4,6 @@ import random import matplotlib.pyplot as plt import numpy as np -import plot_util - class GA(object): def __init__(self, num_drones, num_city, num_total, data, to_process_idx, rectangles): @@ -30,7 +28,6 @@ class GA(object): init_best = self.fruits[sort_index[0]] init_best = self.location[init_best] - # 存储每个iteration的结果，画出收敛图 self.iter_x = [0] self.iter_y = [1.0 / scores[sort_index[0]]] @@ -130,7 +127,7 @@ class GA(object): for k in range(from_index, end_index + 1): car_path.append(path[k]) car_paths.append(car_path) - + # 计算各系统的车辆移动距离 for car_path in car_paths: car_time = 0 @@ -140,7 +137,7 @@ class GA(object): if a in self.to_process_idx and b in self.to_process_idx: car_time += 0 else: - car_time += dis_mat[a][b] * 100 # TODO 这里要换成对应参数 + car_time += dis_mat[a][b] * 100 # TODO 这里要换成对应参数 car_move_info = {'car_path': car_path, 'car_time': car_time} car_infos.append(car_move_info) @@ -164,12 +161,13 @@ class GA(object): if point in self.to_process_idx: continue else: - flight_time += self.rectangles[point - 1]['flight_time'] # 注意，这里要减一！！！ + # 注意，这里要减一！！！ + flight_time += self.rectangles[point - 1]['flight_time'] bs_time += self.rectangles[point - 1]['bs_time'] system_time = max(flight_time + car_info['car_time'], bs_time) T_k_list.append(system_time) T_max = max(T_k_list) - + return T_max # 计算种群适应度 @@ -231,7 +229,7 @@ class GA(object): def ga_parent(self, scores, ga_choose_ratio): sort_index = np.argsort(-scores).copy() - sort_index = sort_index[0 : int(ga_choose_ratio * len(sort_index))] + sort_index = sort_index[0: int(ga_choose_ratio * len(sort_index))] parents = [] parents_score = [] for index in sort_index: @@ -323,47 +321,47 @@ class GA(object): # print(1.0 / best_score) return tmp_best_one, 1.0 / best_score -if __name__ == '__main__': - seed = 42 - num_drones = 6 - num_city = 12 - epochs = 3000 +# if __name__ == '__main__': +# seed = 42 +# num_drones = 6 +# num_city = 12 +# epochs = 3000 - # 固定随机数 - np.random.seed(seed) - random.seed(seed) +# # 固定随机数 +# np.random.seed(seed) +# random.seed(seed) - ## 初始化坐标 (第一个点是基地的起点，起点的坐标是 0,0 ) - data = [[0, 0]] - for i in range(num_city - 1): - while True: - x = np.random.randint(-250, 250) - y = np.random.randint(-250, 250) - if x != 0 or y != 0: - break - data.append([x, y]) +# ## 初始化坐标 (第一个点是基地的起点，起点的坐标是 0,0 ) +# data = [[0, 0]] +# for i in range(num_city - 1): +# while True: +# x = np.random.randint(-250, 250) +# y = np.random.randint(-250, 250) +# if x != 0 or y != 0: +# break +# data.append([x, y]) - data = np.array(data) +# data = np.array(data) - # 关键：有N架无人机，则再增加N-1个`点` (坐标是起始点)，这些点之间的距离是inf - for d in range(num_drones - 1): - data = np.vstack([data, data[0]]) - num_city += 1 # 增加欺骗城市 +# # 关键：有N架无人机，则再增加N-1个`点` (坐标是起始点)，这些点之间的距离是inf +# for d in range(num_drones - 1): +# data = np.vstack([data, data[0]]) +# num_city += 1 # 增加欺骗城市 - to_process_idx = [0] - # print("start point:", location[0]) - for d in range(1, num_drones): # 1, ... drone-1 - # print("added base point:", location[num_city - d]) - to_process_idx.append(num_city - d) +# to_process_idx = [0] +# # print("start point:", location[0]) +# for d in range(1, num_drones): # 1, ... drone-1 +# # print("added base point:", location[num_city - d]) +# to_process_idx.append(num_city - d) - model = GA(num_city=data.shape[0], num_total=20, data=data.copy()) - Best_path, Best = model.run() - print(Best_path) - iterations = model.iter_x - best_record = model.iter_y +# model = GA(num_city=data.shape[0], num_total=20, data=data.copy()) +# Best_path, Best = model.run() +# print(Best_path) +# iterations = model.iter_x +# best_record = model.iter_y - # print(Best_path) +# # print(Best_path) - print(f"Best Path Length: {Best:.3f}") - plot_util.plot_results(Best_path, iterations, best_record) +# print(f"Best Path Length: {Best:.3f}") +# plot_util.plot_results(Best_path, iterations, best_record) diff --git a/GA/hybrid_solver.py b/GA/hybrid_solver.py index 55e89b2..e61808f 100644 --- a/GA/hybrid_solver.py +++ b/GA/hybrid_solver.py @@ -4,7 +4,6 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np from ga import GA -import plot_util # 固定随机种子，便于复现 random.seed(42) diff --git a/GA/main.py b/GA/main.py index ae9131b..f1f07ad 100644 --- a/GA/main.py +++ b/GA/main.py @@ -14,8 +14,8 @@ best_solution = None best_row_boundaries = None best_col_boundaries = None -params_file = 'params2.yml' -with open(params_file, 'r', encoding='utf-8') as file: +params_file = 'params3' +with open(params_file + 'yml', 'r', encoding='utf-8') as file: params = yaml.safe_load(file) H = params['H'] @@ -37,13 +37,13 @@ battery_energy_capacity = params['battery_energy_capacity'] numbers = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] # 生成所有的排列情况（取三次，每次都可以从10个数中选） -row_product = list(product(numbers, repeat=3)) +row_product = list(product(numbers, repeat=1)) # 对每种情况从小到大排序，并剔除重复的情况 row_cuts_set = set( tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product) row_cuts_set = sorted(row_cuts_set) -col_product = list(product(numbers, repeat=3)) +col_product = list(product(numbers, repeat=1)) col_cuts_set = set( tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product) col_cuts_set = sorted(col_cuts_set) @@ -56,13 +56,15 @@ with tqdm(total=total_iterations, desc="Processing") as pbar: col_boundaries = [0.0] + list(col_cuts) + [1.0] # 这里面的距离不再是比例，而是真实距离！ - rectrangles = if_valid_partition(row_boundaries, col_boundaries, params) + rectrangles = if_valid_partition( + row_boundaries, col_boundaries, params) if not rectrangles: pbar.update(1) continue else: # 使用遗传算法求出每一种网格划分的可行解，然后选择其中的最优解 - current_solution, current_time, to_process_idx = GA_solver(rectrangles, k) + current_solution, current_time, to_process_idx = GA_solver( + rectrangles, params) if current_time < best_T: best_T = current_time @@ -83,8 +85,10 @@ with tqdm(total=total_iterations, desc="Processing") as pbar: for k in range(from_index, end_index + 1): rectrangle_idx = best_solution[k] if rectrangle_idx not in to_process_idx: - car_path.append(rectrangles[rectrangle_idx - 1]['center']) - car_paths.append(car_path) + car_path.append( + rectrangles[rectrangle_idx - 1]['center']) + if car_path: + car_paths.append(car_path) pbar.update(1) # 输出最佳方案 @@ -94,9 +98,9 @@ print("Row boundaries:", best_row_boundaries) print("Col boundaries:", best_col_boundaries) output_data = { - 'row_boundaries': row_boundaries, - 'col_boundaries': col_boundaries, + 'row_boundaries': best_row_boundaries, + 'col_boundaries': best_col_boundaries, 'car_paths': car_paths } -with open(f'./solutions/traverse_ga_{params_file}.json', 'w', encoding='utf-8') as file: - json.dump(output_data, file, ensure_ascii=False, indent=4) \ No newline at end of file +with open(f'./solutions/trav_ga_{params_file}.json', 'w', encoding='utf-8') as file: + json.dump(output_data, file, ensure_ascii=False, indent=4) diff --git a/GA/main_parallel.py b/GA/main_parallel.py index cb815c8..453544d 100644 --- a/GA/main_parallel.py +++ b/GA/main_parallel.py @@ -8,46 +8,8 @@ import json from tqdm import tqdm from concurrent.futures import ProcessPoolExecutor, as_completed -np.random.seed(42) -random.seed(42) - -params_file = 'params2.yml' -with open(params_file, 'r', encoding='utf-8') as file: - params = yaml.safe_load(file) - -H = params['H'] -W = params['W'] -k = params['num_cars'] - -flight_time_factor = params['flight_time_factor'] -comp_time_factor = params['comp_time_factor'] -trans_time_factor = params['trans_time_factor'] -car_time_factor = params['car_time_factor'] -bs_time_factor = params['bs_time_factor'] - -flight_energy_factor = params['flight_energy_factor'] -comp_energy_factor = params['comp_energy_factor'] -trans_energy_factor = params['trans_energy_factor'] -battery_energy_capacity = params['battery_energy_capacity'] - -# 定义数字列表 -numbers = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] - -# 生成所有的排列情况（取三次，每次都可以从10个数中选） -row_product = list(product(numbers, repeat=3)) -# 对每种情况从小到大排序，并剔除重复的情况 -row_cuts_set = set( - tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product) -row_cuts_set = sorted(row_cuts_set) - -col_product = list(product(numbers, repeat=3)) -col_cuts_set = set( - tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product) -col_cuts_set = sorted(col_cuts_set) - - -def process_partition(row_cuts, col_cuts): +def process_partition(row_cuts, col_cuts, params): row_boundaries = [0.0] + list(row_cuts) + [1.0] col_boundaries = [0.0] + list(col_cuts) + [1.0] rectrangles = if_valid_partition(row_boundaries, col_boundaries, params) @@ -55,19 +17,58 @@ def process_partition(row_cuts, col_cuts): if not rectrangles: return None # 过滤无效划分 - current_solution, current_time, to_process_idx = GA_solver(rectrangles, k) + current_solution, current_time, to_process_idx = GA_solver( + rectrangles, params) return (current_solution, current_time, row_boundaries, col_boundaries, to_process_idx, rectrangles) if __name__ == "__main__": # 重要：在 Windows 上必须加这一行 + np.random.seed(42) + random.seed(42) + + params_file = 'params3' + with open(params_file + '.yml', 'r', encoding='utf-8') as file: + params = yaml.safe_load(file) + + H = params['H'] + W = params['W'] + k = params['num_cars'] + + flight_time_factor = params['flight_time_factor'] + comp_time_factor = params['comp_time_factor'] + trans_time_factor = params['trans_time_factor'] + car_time_factor = params['car_time_factor'] + bs_time_factor = params['bs_time_factor'] + + flight_energy_factor = params['flight_energy_factor'] + comp_energy_factor = params['comp_energy_factor'] + trans_energy_factor = params['trans_energy_factor'] + battery_energy_capacity = params['battery_energy_capacity'] + + # 定义数字列表 + numbers = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] + + # 生成所有的排列情况（取三次，每次都可以从10个数中选） + row_product = list(product(numbers, repeat=1)) + # 对每种情况从小到大排序，并剔除重复的情况 + row_cuts_set = set( + tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product) + row_cuts_set = sorted(row_cuts_set) + + col_product = list(product(numbers, repeat=1)) + col_cuts_set = set( + tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product) + col_cuts_set = sorted(col_cuts_set) + best_T = float('inf') best_solution = None best_row_boundaries = None best_col_boundaries = None batch_size = 60 # 控制一次最多并行多少个任务 - all_tasks = [(row_cuts, col_cuts) for row_cuts in row_cuts_set for col_cuts in col_cuts_set] + all_tasks = [(row_cuts, col_cuts) + for row_cuts in row_cuts_set for col_cuts in col_cuts_set] total_iterations = len(all_tasks) with ProcessPoolExecutor(max_workers=batch_size) as executor: @@ -82,7 +83,7 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行 pbar.update(1) # 更新进度条 futures.clear() # 清空已完成的任务 - futures.add(executor.submit(process_partition, *task)) # 提交新任务 + futures.add(executor.submit(process_partition, *task, params)) # 提交新任务 # 处理剩余未完成的任务 for future in as_completed(futures): @@ -112,8 +113,10 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行 for k in range(from_index, end_index + 1): rectrangle_idx = best_solution[k] if rectrangle_idx not in to_process_idx: - car_path.append(rectrangles[rectrangle_idx - 1]['center']) - car_paths.append(car_path) + car_path.append( + rectrangles[rectrangle_idx - 1]['center']) + if car_path: + car_paths.append(car_path) # 输出最佳方案 print("Best solution:", best_solution) @@ -122,9 +125,9 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行 print("Col boundaries:", best_col_boundaries) output_data = { - 'row_boundaries': row_boundaries, - 'col_boundaries': col_boundaries, + 'row_boundaries': best_row_boundaries, + 'col_boundaries': best_col_boundaries, 'car_paths': car_paths } - with open(f'./solutions/travse_ga_{params_file}.json', 'w', encoding='utf-8') as file: + with open(f'./solutions/trav_ga_{params_file}_parallel.json', 'w', encoding='utf-8') as file: json.dump(output_data, file, ensure_ascii=False, indent=4) diff --git a/GA/plot_util.py b/GA/plot_util.py deleted file mode 100644 index 04fd216..0000000 --- a/GA/plot_util.py +++ /dev/null @@ -1,93 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np - -# from matplotlib import colors as mcolors - -# matplotlib_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys()) - -matplotlib_colors = [ - "black", - "red", - "yellow", - "grey", - "brown", - "darkred", - "peru", - "darkorange", - "darkkhaki", - "steelblue", - "blue", - "cyan", - "green", - "navajowhite", - "lightgrey", - "lightcoral", - "mediumblue", - "midnightblue", - "blueviolet", - "violet", - "fuchsia", - "mediumvioletred", - "hotpink", - "crimson", - "lightpink", - "slategray", - "lime", - "springgreen", - "teal", - "beige", - "olive", -] - - -def find_indices(list_to_check, item_to_find): - indices = [] - for idx, value in enumerate(list_to_check): - if np.array_equal(value, item_to_find): - indices.append(idx) - return indices - - -def plot_results(Best_path, iterations, best_record): - # print(find_indices(Best_path, [0, 0])) - - # Best_path = np.vstack([Best_path, Best_path[0]]) - # Best_path = np.vstack([Best_path[0], Best_path]) - # print(Best_path[0], Best_path[-1]) - - if not np.array_equal(Best_path[0], [0, 0]): - Best_path = np.vstack([[0, 0], Best_path]) - if not np.array_equal(Best_path[-1], [0, 0]): - Best_path = np.vstack([Best_path, [0, 0]]) - # print(Best_path) - - found_start_points_indices = find_indices(Best_path, [0, 0]) - result_paths = [] - - for j in range(len(found_start_points_indices) - 1): - from_index = found_start_points_indices[j] - end_index = found_start_points_indices[j + 1] - path = [] - for k in range(from_index, end_index + 1): - path.append(Best_path[k]) - path = np.array(path) - result_paths.append(path) - - # print(Best_path) - # print(result_paths) - - fig, axs = plt.subplots(1, 2, sharex=False, sharey=False) - axs[0].scatter(Best_path[:, 0], Best_path[:, 1]) - - for ix, path in enumerate(result_paths): - axs[0].plot(path[:, 0], path[:, 1], color=matplotlib_colors[ix], alpha=0.8) - # axs[0].plot(Best_path[:, 0], Best_path[:, 1], color="green", alpha=0.1) - - # Draw start point - axs[0].plot([0], [0], marker="*", markersize=20, color="red") - - axs[0].set_title("Searched Best Solution") - - axs[1].plot(iterations, best_record) - axs[1].set_title("Convergence Curve") - plt.show() diff --git a/GA/readme.md b/GA/readme.md new file mode 100644 index 0000000..fbd6e14 --- /dev/null +++ b/GA/readme.md @@ -0,0 +1 @@ +参考代码`https://github.com/yhzhu99/mtsp` \ No newline at end of file diff --git a/GA/utils.py b/GA/utils.py index 35065b8..1407d80 100644 --- a/GA/utils.py +++ b/GA/utils.py @@ -59,11 +59,12 @@ def if_valid_partition(row_boundaries, col_boundaries, params): return rectangles -def GA_solver(rectangles, k): +def GA_solver(rectangles, params): num_city = len(rectangles) + 1 # 划分好的区域中心点+整个区域的中心 + k = params['num_cars'] # 初始化坐标 (第一个点是整个区域的中心) - center_data = [[1 / 2.0, 1 / 2.0]] + center_data = [[params['H'] / 2.0, params['W'] / 2.0]] for rec in rectangles: center_data.append(rec['center']) center_data = np.array(center_data) @@ -79,7 +80,7 @@ def GA_solver(rectangles, k): # print("added base point:", location[num_city - d]) to_process_idx.append(num_city - d) - model = GA(num_drones=k, num_city=center_data.shape[0], num_total=20, data=center_data.copy( + model = GA(num_drones=k, num_city=num_city, num_total=20, data=center_data.copy( ), to_process_idx=to_process_idx, rectangles=rectangles) Best_path, Best = model.run() diff --git a/params3.yml b/params3.yml new file mode 100644 index 0000000..b3a45fe --- /dev/null +++ b/params3.yml @@ -0,0 +1,16 @@ +H : 30 # 区域高度，网格点之间的距离为25m（单位距离） +W : 30 # 区域宽度 +num_cars : 2 # 系统数量（车-巢-机系统个数） + +# 时间系数（单位：秒，每个网格一张照片） +flight_time_factor : 3 # 每张照片对应的飞行时间，无人机飞行速度为9.5m/s，拍摄照片的时间间隔为3s +comp_time_factor : 5 # 无人机上每张照片计算时间，5s +trans_time_factor : 0.3 # 每张照片传输时间，0.3s +car_time_factor : 100 # TODO 汽车每单位距离的移动时间，2s，加了一个放大因子50 +bs_time_factor : 5 # 机巢上每张照片计算时间 + +# 其他参数 +flight_energy_factor : 0.05 # 单位：分钟/张 +comp_energy_factor : 0.05 # TODO 计算能耗需要重新估计 +trans_energy_factor : 0.0025 +battery_energy_capacity : 20 # 无人机只进行飞行，续航为30分钟 \ No newline at end of file diff --git a/visualization.py b/visualization.py index 75ad87a..f3768c4 100644 --- a/visualization.py +++ b/visualization.py @@ -7,7 +7,7 @@ def visualize_solution(row_boundaries, col_boundaries, car_paths, W, H): plt.rcParams['font.sans-serif'] = ['SimHei'] fig, ax = plt.subplots() ax.set_xlim(0, W) - ax.set_ylim(0, H) + ax.set_ylim(H, 0) # 调整y轴方向，原点在左上角 ax.set_title("区域划分与车-机-巢系统覆盖") ax.set_xlabel("区域宽度") ax.set_ylabel("区域高度") @@ -16,7 +16,7 @@ def visualize_solution(row_boundaries, col_boundaries, car_paths, W, H): colors = ['red', 'blue', 'green', 'orange', 'purple', 'cyan', 'magenta'] # 绘制区域中心 - region_center = (H / 2.0, W / 2.0) # 注意：x对应宽度，y对应高度 + region_center = (H / 2.0, W / 2.0) ax.plot(region_center[1], region_center[0], 'ko', markersize=8, label="区域中心") @@ -36,22 +36,20 @@ def visualize_solution(row_boundaries, col_boundaries, car_paths, W, H): # 添加图例 ax.legend() - # 反转 y 轴使得行号从上到下递增（如需，可取消） - ax.invert_yaxis() plt.show() if __name__ == "__main__": import yaml # 读取参数 - with open('params.yml', 'r', encoding='utf-8') as file: + with open('params3.yml', 'r', encoding='utf-8') as file: params = yaml.safe_load(file) H = params['H'] W = params['W'] # 读取最佳方案的JSON文件 - with open('./solutions/best_solution_mtkl.json', 'r', encoding='utf-8') as f: + with open(r'solutions\trav_ga_params3_parallel.json', 'r', encoding='utf-8') as f: best_solution = json.load(f) row_boundaries = best_solution['row_boundaries']