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修改GA bug

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weixin_46229132 2025-03-22 21:43:11 +08:00
parent 17acfa5409
commit 8e8d9a25df
9 changed files with 132 additions and 205 deletions
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78
GA/ga.py
View File

@ -4,8 +4,6 @@ import random
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import plot_util
class GA(object): class GA(object):
def __init__(self, num_drones, num_city, num_total, data, to_process_idx, rectangles): 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.fruits[sort_index[0]]
init_best = self.location[init_best] init_best = self.location[init_best]
# 存储每个iteration的结果，画出收敛图 # 存储每个iteration的结果，画出收敛图
self.iter_x = [0] self.iter_x = [0]
self.iter_y = [1.0 / scores[sort_index[0]]] self.iter_y = [1.0 / scores[sort_index[0]]]
@ -164,7 +161,8 @@ class GA(object):
if point in self.to_process_idx: if point in self.to_process_idx:
continue continue
else: 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'] bs_time += self.rectangles[point - 1]['bs_time']
system_time = max(flight_time + car_info['car_time'], bs_time) system_time = max(flight_time + car_info['car_time'], bs_time)
T_k_list.append(system_time) T_k_list.append(system_time)
@ -231,7 +229,7 @@ class GA(object):
def ga_parent(self, scores, ga_choose_ratio): def ga_parent(self, scores, ga_choose_ratio):
sort_index = np.argsort(-scores).copy() 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 = []
parents_score = [] parents_score = []
for index in sort_index: for index in sort_index:
@ -323,47 +321,47 @@ class GA(object):
# print(1.0 / best_score) # print(1.0 / best_score)
return tmp_best_one, 1.0 / best_score return tmp_best_one, 1.0 / best_score
if __name__ == '__main__': # if __name__ == '__main__':
seed = 42 # seed = 42
num_drones = 6 # num_drones = 6
num_city = 12 # num_city = 12
epochs = 3000 # epochs = 3000
# 固定随机数 # # 固定随机数
np.random.seed(seed) # np.random.seed(seed)
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 ) # ## 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]] # data = [[0, 0]]
for i in range(num_city - 1): # for i in range(num_city - 1):
while True: # while True:
x = np.random.randint(-250, 250) # x = np.random.randint(-250, 250)
y = np.random.randint(-250, 250) # y = np.random.randint(-250, 250)
if x != 0 or y != 0: # if x != 0 or y != 0:
break # break
data.append([x, y]) # 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 # # 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): # for d in range(num_drones - 1):
data = np.vstack([data, data[0]]) # data = np.vstack([data, data[0]])
num_city += 1 # 增加欺骗城市 # num_city += 1 # 增加欺骗城市
to_process_idx = [0] # to_process_idx = [0]
# print("start point:", location[0]) # # print("start point:", location[0])
for d in range(1, num_drones): # 1, ... drone-1 # for d in range(1, num_drones): # 1, ... drone-1
# print("added base point:", location[num_city - d]) # # print("added base point:", location[num_city - d])
to_process_idx.append(num_city - d) # to_process_idx.append(num_city - d)
model = GA(num_city=data.shape[0], num_total=20, data=data.copy()) # model = GA(num_city=data.shape[0], num_total=20, data=data.copy())
Best_path, Best = model.run() # Best_path, Best = model.run()
print(Best_path) # print(Best_path)
iterations = model.iter_x # iterations = model.iter_x
best_record = model.iter_y # best_record = model.iter_y
# print(Best_path) # # print(Best_path)
print(f"Best Path Length: {Best:.3f}") # print(f"Best Path Length: {Best:.3f}")
plot_util.plot_results(Best_path, iterations, best_record) # plot_util.plot_results(Best_path, iterations, best_record)

1
GA/hybrid_solver.py
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@ -4,7 +4,6 @@ import matplotlib.pyplot as plt
import matplotlib.patches as patches import matplotlib.patches as patches
import numpy as np import numpy as np
from ga import GA from ga import GA
import plot_util
# 固定随机种子，便于复现 # 固定随机种子，便于复现
random.seed(42) random.seed(42)

24
GA/main.py
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@ -14,8 +14,8 @@ best_solution = None
best_row_boundaries = None best_row_boundaries = None
best_col_boundaries = None best_col_boundaries = None
params_file = 'params2.yml' params_file = 'params3'
with open(params_file, 'r', encoding='utf-8') as file: with open(params_file + 'yml', 'r', encoding='utf-8') as file:
params = yaml.safe_load(file) params = yaml.safe_load(file)
H = params['H'] 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] 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） # 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 # 对每种情况从小到大排序，并剔除重复的情况
row_cuts_set = set( row_cuts_set = set(
tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product) tuple(sorted(set(item for item in prod if item > 0))) for prod in row_product)
row_cuts_set = sorted(row_cuts_set) 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( col_cuts_set = set(
tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product) tuple(sorted(set(item for item in prod if item > 0))) for prod in col_product)
col_cuts_set = sorted(col_cuts_set) 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] 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！ # 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: if not rectrangles:
pbar.update(1) pbar.update(1)
continue continue
else: 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 # 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: if current_time < best_T:
best_T = current_time best_T = current_time
@ -83,7 +85,9 @@ with tqdm(total=total_iterations, desc="Processing") as pbar:
for k in range(from_index, end_index + 1): for k in range(from_index, end_index + 1):
rectrangle_idx = best_solution[k] rectrangle_idx = best_solution[k]
if rectrangle_idx not in to_process_idx: if rectrangle_idx not in to_process_idx:
car_path.append(rectrangles[rectrangle_idx - 1]['center']) car_path.append(
rectrangles[rectrangle_idx - 1]['center'])
if car_path:
car_paths.append(car_path) car_paths.append(car_path)
pbar.update(1) pbar.update(1)
@ -94,9 +98,9 @@ print("Row boundaries:", best_row_boundaries)
print("Col boundaries:", best_col_boundaries) print("Col boundaries:", best_col_boundaries)
output_data = { output_data = {
'row_boundaries': row_boundaries, 'row_boundaries': best_row_boundaries,
'col_boundaries': col_boundaries, 'col_boundaries': best_col_boundaries,
'car_paths': car_paths 'car_paths': car_paths
} }
with open(f'./solutions/traverse_ga_{params_file}.json', 'w', encoding='utf-8') as 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) json.dump(output_data, file, ensure_ascii=False, indent=4)

95
GA/main_parallel.py
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@ -8,46 +8,8 @@ import json
from tqdm import tqdm from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed from concurrent.futures import ProcessPoolExecutor, as_completed
np.random.seed(42)
random.seed(42)
def process_partition(row_cuts, col_cuts, params):
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):
row_boundaries = [0.0] + list(row_cuts) + [1.0] row_boundaries = [0.0] + list(row_cuts) + [1.0]
col_boundaries = [0.0] + list(col_cuts) + [1.0] col_boundaries = [0.0] + list(col_cuts) + [1.0]
rectrangles = if_valid_partition(row_boundaries, col_boundaries, params) rectrangles = if_valid_partition(row_boundaries, col_boundaries, params)
@ -55,19 +17,58 @@ def process_partition(row_cuts, col_cuts):
if not rectrangles: if not rectrangles:
return None # 过滤无效划分 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) 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 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_T = float('inf')
best_solution = None best_solution = None
best_row_boundaries = None best_row_boundaries = None
best_col_boundaries = None best_col_boundaries = None
batch_size = 60 # 控制一次最多并行多少个任务 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) total_iterations = len(all_tasks)
with ProcessPoolExecutor(max_workers=batch_size) as executor: 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 pbar.update(1) # 更新进度条
futures.clear() # 清空已完成的任务 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 # 处理剩余未完成的任务
for future in as_completed(futures): for future in as_completed(futures):
@ -112,7 +113,9 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
for k in range(from_index, end_index + 1): for k in range(from_index, end_index + 1):
rectrangle_idx = best_solution[k] rectrangle_idx = best_solution[k]
if rectrangle_idx not in to_process_idx: if rectrangle_idx not in to_process_idx:
car_path.append(rectrangles[rectrangle_idx - 1]['center']) car_path.append(
rectrangles[rectrangle_idx - 1]['center'])
if car_path:
car_paths.append(car_path) car_paths.append(car_path)
# 输出最佳方案 # 输出最佳方案
@ -122,9 +125,9 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
print("Col boundaries:", best_col_boundaries) print("Col boundaries:", best_col_boundaries)
output_data = { output_data = {
'row_boundaries': row_boundaries, 'row_boundaries': best_row_boundaries,
'col_boundaries': col_boundaries, 'col_boundaries': best_col_boundaries,
'car_paths': car_paths '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) json.dump(output_data, file, ensure_ascii=False, indent=4)

93
GA/plot_util.py
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@ -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()

1
GA/readme.md Normal file
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@ -0,0 +1 @@
参考代码`https://github.com/yhzhu99/mtsp`

7
GA/utils.py
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@ -59,11 +59,12 @@ def if_valid_partition(row_boundaries, col_boundaries, params):
return rectangles 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 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) # 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: for rec in rectangles:
center_data.append(rec['center']) center_data.append(rec['center'])
center_data = np.array(center_data) center_data = np.array(center_data)
@ -79,7 +80,7 @@ def GA_solver(rectangles, k):
# print("added base point:", location[num_city - d]) # print("added base point:", location[num_city - d])
to_process_idx.append(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) ), to_process_idx=to_process_idx, rectangles=rectangles)
Best_path, Best = model.run() Best_path, Best = model.run()

16
params3.yml Normal file
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@ -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

10
visualization.py
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@ -7,7 +7,7 @@ def visualize_solution(row_boundaries, col_boundaries, car_paths, W, H):
plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['font.sans-serif'] = ['SimHei']
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.set_xlim(0, W) 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_title("区域划分与车-机-巢系统覆盖")
ax.set_xlabel("区域宽度") ax.set_xlabel("区域宽度")
ax.set_ylabel("区域高度") 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'] colors = ['red', 'blue', 'green', 'orange', 'purple', 'cyan', 'magenta']
# 绘制区域中心 # 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], ax.plot(region_center[1], region_center[0],
'ko', markersize=8, label="区域中心") '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 # 添加图例
ax.legend() 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() plt.show()
if __name__ == "__main__": if __name__ == "__main__":
import yaml import yaml
# 读取参数 # 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) params = yaml.safe_load(file)
H = params['H'] H = params['H']
W = params['W'] W = params['W']
# 读取最佳方案的JSON文件 # 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) best_solution = json.load(f)
row_boundaries = best_solution['row_boundaries'] row_boundaries = best_solution['row_boundaries']