修改GA bug
This commit is contained in:
weixin_46229132
parent
17acfa5409
commit
8e8d9a25df
76
GA/ga.py
76
GA/ga.py
@ -4,8 +4,6 @@ import random
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import matplotlib.pyplot as plt
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import numpy as np
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import plot_util
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class GA(object):
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def __init__(self, num_drones, num_city, num_total, data, to_process_idx, rectangles):
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@ -30,7 +28,6 @@ class GA(object):
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init_best = self.fruits[sort_index[0]]
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init_best = self.location[init_best]
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# 存储每个iteration的结果，画出收敛图
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self.iter_x = [0]
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self.iter_y = [1.0 / scores[sort_index[0]]]
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@ -164,7 +161,8 @@ class GA(object):
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if point in self.to_process_idx:
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continue
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else:
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flight_time += self.rectangles[point - 1]['flight_time'] # 注意，这里要减一！！！
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# 注意，这里要减一！！！
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flight_time += self.rectangles[point - 1]['flight_time']
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bs_time += self.rectangles[point - 1]['bs_time']
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system_time = max(flight_time + car_info['car_time'], bs_time)
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T_k_list.append(system_time)
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@ -323,47 +321,47 @@ class GA(object):
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# print(1.0 / best_score)
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return tmp_best_one, 1.0 / best_score
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if __name__ == '__main__':
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seed = 42
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num_drones = 6
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num_city = 12
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epochs = 3000
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# if __name__ == '__main__':
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# seed = 42
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# num_drones = 6
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# num_city = 12
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# epochs = 3000
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# 固定随机数
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np.random.seed(seed)
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random.seed(seed)
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# # 固定随机数
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# np.random.seed(seed)
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# random.seed(seed)
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## 初始化坐标 (第一个点是基地的起点，起点的坐标是 0,0 )
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data = [[0, 0]]
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for i in range(num_city - 1):
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while True:
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x = np.random.randint(-250, 250)
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y = np.random.randint(-250, 250)
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if x != 0 or y != 0:
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break
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data.append([x, y])
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# ## 初始化坐标 (第一个点是基地的起点，起点的坐标是 0,0 )
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# data = [[0, 0]]
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# for i in range(num_city - 1):
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# while True:
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# x = np.random.randint(-250, 250)
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# y = np.random.randint(-250, 250)
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# if x != 0 or y != 0:
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# break
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# data.append([x, y])
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data = np.array(data)
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# data = np.array(data)
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# 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
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for d in range(num_drones - 1):
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data = np.vstack([data, data[0]])
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num_city += 1 # 增加欺骗城市
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# # 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
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# for d in range(num_drones - 1):
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# data = np.vstack([data, data[0]])
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# num_city += 1 # 增加欺骗城市
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to_process_idx = [0]
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# print("start point:", location[0])
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for d in range(1, num_drones): # 1, ... drone-1
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# print("added base point:", location[num_city - d])
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to_process_idx.append(num_city - d)
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model = GA(num_city=data.shape[0], num_total=20, data=data.copy())
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Best_path, Best = model.run()
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print(Best_path)
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iterations = model.iter_x
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best_record = model.iter_y
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# to_process_idx = [0]
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# # print("start point:", location[0])
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# for d in range(1, num_drones): # 1, ... drone-1
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# # print("added base point:", location[num_city - d])
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# to_process_idx.append(num_city - d)
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# model = GA(num_city=data.shape[0], num_total=20, data=data.copy())
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# Best_path, Best = model.run()
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# print(Best_path)
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# iterations = model.iter_x
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# best_record = model.iter_y
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print(f"Best Path Length: {Best:.3f}")
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plot_util.plot_results(Best_path, iterations, best_record)
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# # print(Best_path)
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# print(f"Best Path Length: {Best:.3f}")
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# plot_util.plot_results(Best_path, iterations, best_record)
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@ -4,7 +4,6 @@ import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import numpy as np
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from ga import GA
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import plot_util
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# 固定随机种子，便于复现
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random.seed(42)
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24
GA/main.py
24
GA/main.py
@ -14,8 +14,8 @@ best_solution = None
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best_row_boundaries = None
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best_col_boundaries = None
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params_file = 'params2.yml'
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with open(params_file, 'r', encoding='utf-8') as file:
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params_file = 'params3'
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with open(params_file + 'yml', 'r', encoding='utf-8') as file:
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params = yaml.safe_load(file)
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H = params['H']
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@ -37,13 +37,13 @@ battery_energy_capacity = params['battery_energy_capacity']
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numbers = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
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# 生成所有的排列情况（取三次，每次都可以从10个数中选）
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row_product = list(product(numbers, repeat=3))
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row_product = list(product(numbers, repeat=1))
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# 对每种情况从小到大排序，并剔除重复的情况
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row_cuts_set = set(
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tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product)
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row_cuts_set = sorted(row_cuts_set)
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col_product = list(product(numbers, repeat=3))
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col_product = list(product(numbers, repeat=1))
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col_cuts_set = set(
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tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product)
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col_cuts_set = sorted(col_cuts_set)
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@ -56,13 +56,15 @@ with tqdm(total=total_iterations, desc="Processing") as pbar:
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col_boundaries = [0.0] + list(col_cuts) + [1.0]
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# 这里面的距离不再是比例，而是真实距离！
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rectrangles = if_valid_partition(row_boundaries, col_boundaries, params)
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rectrangles = if_valid_partition(
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row_boundaries, col_boundaries, params)
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if not rectrangles:
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pbar.update(1)
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continue
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else:
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# 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
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current_solution, current_time, to_process_idx = GA_solver(rectrangles, k)
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current_solution, current_time, to_process_idx = GA_solver(
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rectrangles, params)
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if current_time < best_T:
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best_T = current_time
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@ -83,7 +85,9 @@ with tqdm(total=total_iterations, desc="Processing") as pbar:
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for k in range(from_index, end_index + 1):
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rectrangle_idx = best_solution[k]
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if rectrangle_idx not in to_process_idx:
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car_path.append(rectrangles[rectrangle_idx - 1]['center'])
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car_path.append(
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rectrangles[rectrangle_idx - 1]['center'])
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if car_path:
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car_paths.append(car_path)
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pbar.update(1)
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@ -94,9 +98,9 @@ print("Row boundaries:", best_row_boundaries)
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print("Col boundaries:", best_col_boundaries)
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output_data = {
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'row_boundaries': row_boundaries,
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'col_boundaries': col_boundaries,
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'row_boundaries': best_row_boundaries,
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'col_boundaries': best_col_boundaries,
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'car_paths': car_paths
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}
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with open(f'./solutions/traverse_ga_{params_file}.json', 'w', encoding='utf-8') as file:
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with open(f'./solutions/trav_ga_{params_file}.json', 'w', encoding='utf-8') as file:
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json.dump(output_data, file, ensure_ascii=False, indent=4)
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@ -8,12 +8,27 @@ import json
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from tqdm import tqdm
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from concurrent.futures import ProcessPoolExecutor, as_completed
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def process_partition(row_cuts, col_cuts, params):
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row_boundaries = [0.0] + list(row_cuts) + [1.0]
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col_boundaries = [0.0] + list(col_cuts) + [1.0]
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rectrangles = if_valid_partition(row_boundaries, col_boundaries, params)
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if not rectrangles:
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return None # 过滤无效划分
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current_solution, current_time, to_process_idx = GA_solver(
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rectrangles, params)
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return (current_solution, current_time, row_boundaries, col_boundaries, to_process_idx, rectrangles)
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if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
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np.random.seed(42)
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random.seed(42)
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params_file = 'params2.yml'
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with open(params_file, 'r', encoding='utf-8') as file:
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params_file = 'params3'
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with open(params_file + '.yml', 'r', encoding='utf-8') as file:
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params = yaml.safe_load(file)
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H = params['H']
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@ -35,39 +50,25 @@ battery_energy_capacity = params['battery_energy_capacity']
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numbers = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
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# 生成所有的排列情况（取三次，每次都可以从10个数中选）
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row_product = list(product(numbers, repeat=3))
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row_product = list(product(numbers, repeat=1))
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# 对每种情况从小到大排序，并剔除重复的情况
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row_cuts_set = set(
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tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product)
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row_cuts_set = sorted(row_cuts_set)
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col_product = list(product(numbers, repeat=3))
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col_product = list(product(numbers, repeat=1))
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col_cuts_set = set(
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tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product)
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col_cuts_set = sorted(col_cuts_set)
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def process_partition(row_cuts, col_cuts):
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row_boundaries = [0.0] + list(row_cuts) + [1.0]
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col_boundaries = [0.0] + list(col_cuts) + [1.0]
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rectrangles = if_valid_partition(row_boundaries, col_boundaries, params)
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if not rectrangles:
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return None # 过滤无效划分
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current_solution, current_time, to_process_idx = GA_solver(rectrangles, k)
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return (current_solution, current_time, row_boundaries, col_boundaries, to_process_idx, rectrangles)
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if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
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best_T = float('inf')
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best_solution = None
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best_row_boundaries = None
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best_col_boundaries = None
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batch_size = 60 # 控制一次最多并行多少个任务
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all_tasks = [(row_cuts, col_cuts) for row_cuts in row_cuts_set for col_cuts in col_cuts_set]
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all_tasks = [(row_cuts, col_cuts)
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for row_cuts in row_cuts_set for col_cuts in col_cuts_set]
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total_iterations = len(all_tasks)
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with ProcessPoolExecutor(max_workers=batch_size) as executor:
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@ -82,7 +83,7 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
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pbar.update(1) # 更新进度条
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futures.clear() # 清空已完成的任务
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futures.add(executor.submit(process_partition, *task)) # 提交新任务
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futures.add(executor.submit(process_partition, *task, params)) # 提交新任务
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# 处理剩余未完成的任务
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for future in as_completed(futures):
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@ -112,7 +113,9 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
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for k in range(from_index, end_index + 1):
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rectrangle_idx = best_solution[k]
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if rectrangle_idx not in to_process_idx:
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car_path.append(rectrangles[rectrangle_idx - 1]['center'])
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car_path.append(
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rectrangles[rectrangle_idx - 1]['center'])
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if car_path:
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car_paths.append(car_path)
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# 输出最佳方案
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@ -122,9 +125,9 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
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print("Col boundaries:", best_col_boundaries)
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output_data = {
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'row_boundaries': row_boundaries,
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'col_boundaries': col_boundaries,
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'row_boundaries': best_row_boundaries,
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'col_boundaries': best_col_boundaries,
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'car_paths': car_paths
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}
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with open(f'./solutions/travse_ga_{params_file}.json', 'w', encoding='utf-8') as file:
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with open(f'./solutions/trav_ga_{params_file}_parallel.json', 'w', encoding='utf-8') as file:
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json.dump(output_data, file, ensure_ascii=False, indent=4)
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@ -1,93 +0,0 @@
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import matplotlib.pyplot as plt
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import numpy as np
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# from matplotlib import colors as mcolors
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# matplotlib_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())
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matplotlib_colors = [
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"black",
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"red",
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"yellow",
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"grey",
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"brown",
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"darkred",
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"peru",
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"darkorange",
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"darkkhaki",
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"steelblue",
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"blue",
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"cyan",
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"green",
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"navajowhite",
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"lightgrey",
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"lightcoral",
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"mediumblue",
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"midnightblue",
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"blueviolet",
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"violet",
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"fuchsia",
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"mediumvioletred",
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"hotpink",
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"crimson",
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"lightpink",
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"slategray",
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"lime",
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"springgreen",
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"teal",
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"beige",
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"olive",
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]
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def find_indices(list_to_check, item_to_find):
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indices = []
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for idx, value in enumerate(list_to_check):
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if np.array_equal(value, item_to_find):
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indices.append(idx)
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return indices
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def plot_results(Best_path, iterations, best_record):
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# print(find_indices(Best_path, [0, 0]))
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# Best_path = np.vstack([Best_path, Best_path[0]])
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# Best_path = np.vstack([Best_path[0], Best_path])
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# print(Best_path[0], Best_path[-1])
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if not np.array_equal(Best_path[0], [0, 0]):
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Best_path = np.vstack([[0, 0], Best_path])
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if not np.array_equal(Best_path[-1], [0, 0]):
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Best_path = np.vstack([Best_path, [0, 0]])
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# print(Best_path)
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found_start_points_indices = find_indices(Best_path, [0, 0])
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result_paths = []
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for j in range(len(found_start_points_indices) - 1):
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from_index = found_start_points_indices[j]
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end_index = found_start_points_indices[j + 1]
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path = []
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for k in range(from_index, end_index + 1):
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path.append(Best_path[k])
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path = np.array(path)
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result_paths.append(path)
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# print(Best_path)
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# print(result_paths)
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fig, axs = plt.subplots(1, 2, sharex=False, sharey=False)
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axs[0].scatter(Best_path[:, 0], Best_path[:, 1])
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for ix, path in enumerate(result_paths):
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axs[0].plot(path[:, 0], path[:, 1], color=matplotlib_colors[ix], alpha=0.8)
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# axs[0].plot(Best_path[:, 0], Best_path[:, 1], color="green", alpha=0.1)
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# Draw start point
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axs[0].plot([0], [0], marker="*", markersize=20, color="red")
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axs[0].set_title("Searched Best Solution")
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axs[1].plot(iterations, best_record)
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axs[1].set_title("Convergence Curve")
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plt.show()
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1
GA/readme.md
Normal file
1
GA/readme.md
Normal file
@ -0,0 +1 @@
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参考代码`https://github.com/yhzhu99/mtsp`
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@ -59,11 +59,12 @@ def if_valid_partition(row_boundaries, col_boundaries, params):
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return rectangles
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def GA_solver(rectangles, k):
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def GA_solver(rectangles, params):
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num_city = len(rectangles) + 1 # 划分好的区域中心点+整个区域的中心
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k = params['num_cars']
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# 初始化坐标 (第一个点是整个区域的中心)
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center_data = [[1 / 2.0, 1 / 2.0]]
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center_data = [[params['H'] / 2.0, params['W'] / 2.0]]
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for rec in rectangles:
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center_data.append(rec['center'])
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||||
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()
|
||||
|
||||
|
||||
16
params3.yml
Normal file
16
params3.yml
Normal file
@ -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
|
||||
@ -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']
|
||||
|
||||
Loading…
Reference in New Issue
Block a user