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修改场景

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This commit is contained in:
weixin_46229132 2025-03-31 14:23:29 +08:00
parent dab8f4fd8f
commit 27829c5d48
13 changed files with 215 additions and 146 deletions
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6
GA/main_parallel.py
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@ -30,10 +30,10 @@ if __name__ == "__main__": # 重要：在 Windows 上必须加这一行
# ---------------------------
# 需要修改的超参数
# ---------------------------
R = 1
R = 3
C = 1
params_file = 'params3'
batch_size = 10 # 控制一次最多并行多少个任务
params_file = 'params_50_50_3'
batch_size = 60 # 控制一次最多并行多少个任务
with open(params_file + '.yml', 'r', encoding='utf-8') as file:
params = yaml.safe_load(file)

4
GA/sa_finetune.py
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@ -225,8 +225,8 @@ if __name__ == "__main__":
# ---------------------------
# 需要修改的超参数
# ---------------------------
solution_path = r"solutions\trav_ga_params2_parallel.json"
params_file = r"params2"
solution_path = r"solutions\trav_ga_params_50_50_3_parallel.json"
params_file = r"params_50_50_3"
max_iterations=10000
initial_temp=100
cooling_rate=0.95

8
mtkl_sovler.py
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@ -15,9 +15,9 @@ num_iterations = 10000
# 随机生成分区的行分段数与列分段数
# R = random.randint(0, 3) # 行分段数
# C = random.randint(0, 3) # 列分段数
R = 1
C = 1
params_file = 'params3'
R = 3
C = 3
params_file = 'params_50_50_3'
with open(params_file + '.yml', 'r', encoding='utf-8') as file:
@ -45,7 +45,7 @@ best_T = float('inf')
best_solution = None
for iteration in range(num_iterations):
# 生成随机的行、列分割边界
# 切比例
horiz = [np.clip(np.floor(random.random() * 10) / 10, 0.0, 0.9)
for _ in range(R)]
horiz = sorted(set(horiz))

189
mtkl_sovler2.py Normal file
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@ -0,0 +1,189 @@
import random
import math
import yaml
import json
import numpy as np
# 固定随机种子，便于复现
random.seed(42)
# ---------------------------
# 需要修改的超参数
# ---------------------------
num_iterations = 10000000000
# 随机生成分区的行分段数与列分段数
R = random.randint(0, 3) # 行分段数
C = random.randint(0, 3) # 列分段数
# R = 3
# C = 3
params_file = 'params_50_50_3'
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']
# ---------------------------
# 蒙特卡洛模拟，寻找最佳方案
# ---------------------------
best_T = float('inf')
best_solution = None
for iteration in range(num_iterations):
# 直接切值
horiz = [random.random() 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 = [random.random() for _ in range(C)]
vert = sorted(set(vert))
vert = vert if vert else []
col_boundaries = [0] + vert + [1]
col_boundaries = [boundary * W for boundary in col_boundaries]
# ---------------------------
# 根据分割边界生成所有矩形任务
# ---------------------------
rectangles = []
valid_partition = True # 标记此分区是否满足所有约束
for i in range(len(row_boundaries) - 1):
for j in range(len(col_boundaries) - 1):
r1 = row_boundaries[i]
r2 = row_boundaries[i + 1]
c1 = col_boundaries[j]
c2 = col_boundaries[j + 1]
d = (r2 - r1) * (c2 - c1) # 任务的照片数量（矩形面积）
# 求解rho
rho_time_limit = (flight_time_factor - trans_time_factor) / \
(comp_time_factor - trans_time_factor)
rho_energy_limit = (battery_energy_capacity - flight_energy_factor * d - trans_energy_factor * d) / \
(comp_energy_factor * d - trans_energy_factor * d)
if rho_energy_limit < 0:
valid_partition = False
break
rho = min(rho_time_limit, rho_energy_limit)
flight_time = flight_time_factor * d
comp_time = comp_time_factor * rho * d
trans_time = trans_time_factor * (1 - rho) * d
bs_time = bs_time_factor * (1 - rho) * d
# 计算任务矩形中心，用于后续车辆移动时间计算
center_r = (r1 + r2) / 2.0
center_c = (c1 + c2) / 2.0
rectangles.append({
'r1': r1, 'r2': r2, 'c1': c1, 'c2': c2,
'd': d,
'rho': rho,
'flight_time': flight_time,
'comp_time': comp_time,
'trans_time': trans_time,
'bs_time': bs_time,
'center': (center_r, center_c)
})
if not valid_partition:
break
# 如果分区中存在任务不满足电池约束，则跳过该分区
if not valid_partition:
continue
# ---------------------------
# 随机将所有矩形任务分配给 k 个系统（车-机-巢）
# ---------------------------
car_paths = [[] for _ in range(k)]
for i in range(len(row_boundaries) - 1):
for j in range(len(col_boundaries) - 1):
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：
# T_k = 所有任务的飞行时间之和 + 车辆的移动时间
# 车辆移动时间：车辆从区域中心出发，依次经过各任务中心（顺序采用距离区域中心的启发式排序）
# ---------------------------
region_center = (H / 2.0, W / 2.0)
T_k_list = []
for i in range(k):
car_path = car_paths[i]
car_path.sort(key=lambda r: math.dist(
rectangles[r]['center'], region_center))
total_flight_time = sum(
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(rectangles[car_path[0]]['center'],
region_center) * car_time_factor
# 依次经过任务中心
for j in range(len(car_path) - 1):
prev_center = rectangles[car_path[j]]['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
car_time += math.dist(region_center, curr_center) * car_time_factor
else:
car_time = 0
# 机巢的计算时间
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)
T_max = max(T_k_list) # 整体目标 T 为各系统中最大的 T_k
# TODO 没有限制系统的总能耗
if T_max < best_T:
best_T = T_max
best_solution = {
'car_paths': car_paths,
'T_k_list': T_k_list,
'T_max': T_max,
'iteration': iteration,
'R': R,
'C': C,
'row_boundaries': row_boundaries,
'col_boundaries': col_boundaries,
'car_time': car_time,
'flight_time': total_flight_time,
'bs_time': total_bs_time
}
# ---------------------------
# 输出最佳方案
# ---------------------------
if best_solution is not None:
print("最佳 T:", best_solution['T_max'])
print("Row boundaries:", best_solution['row_boundaries'])
print("Col boundaries:", best_solution['col_boundaries'])
print("最佳路径:", best_solution['car_paths'])
# 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': 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)
else:
print("在给定的模拟次数内未找到满足所有约束的方案。")

16
params3.yml
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@ -1,16 +0,0 @@
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

0
params2.yml → params_50_50_3.yml
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20
solutions/finetune_params2.json → solutions/finetune_params_50_50_3.json
View File

@ -1,7 +1,7 @@
{
"row_boundaries": [
0.0,
0.3000000000000001,
0.30000000000000016,
0.4800000000000001,
0.77,
1.0
@ -12,19 +12,19 @@
1.0
],
"car_paths": [
[
0,
2,
4
],
[
6,
7
],
[
1,
3,
5
],
[
4,
2,
0
],
[
7,
6
]
]
}

30
solutions/mtkl_params2.json
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@ -1,30 +0,0 @@
{
"row_boundaries": [
0.0,
0.5,
1.0
],
"col_boundaries": [
0.0,
0.2,
0.5,
0.8,
1.0
],
"car_paths": [
[
2,
3,
7
],
[
1,
5,
6
],
[
0,
4
]
]
}

22
solutions/mtkl_params3.json
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@ -1,22 +0,0 @@
{
"row_boundaries": [
0.0,
0.5,
1.0
],
"col_boundaries": [
0.0,
0.5,
1.0
],
"car_paths": [
[
0,
2
],
[
1,
3
]
]
}

30
solutions/trav_ga_params2.json
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@ -1,30 +0,0 @@
{
"row_boundaries": [
0.0,
0.1,
0.4,
0.7,
1.0
],
"col_boundaries": [
0.0,
0.5,
1.0
],
"car_paths": [
[
0,
2,
4
],
[
5,
3,
1
],
[
7,
6
]
]
}

22
solutions/trav_ga_params3_parallel.json
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@ -1,22 +0,0 @@
{
"row_boundaries": [
0.0,
0.5,
1.0
],
"col_boundaries": [
0.0,
0.5,
1.0
],
"car_paths": [
[
0,
2
],
[
3,
1
]
]
}

10
solutions/trav_ga_params2_parallel.json → solutions/trav_ga_params_50_50_3_parallel.json
View File

@ -12,11 +12,6 @@
1.0
],
"car_paths": [
[
1,
3,
5
],
[
0,
2,
@ -25,6 +20,11 @@
[
6,
7
],
[
1,
3,
5
]
]
}

4
visualization.py
View File

@ -52,8 +52,8 @@ if __name__ == "__main__":
# ---------------------------
# 需要修改的超参数
# ---------------------------
params_file = 'params2'
solution_file = r'solutions\trav_finetune_params2.json'
params_file = 'params3'
solution_file = r'solutions\trav_ga_params3_parallel.json'
with open(params_file + '.yml', 'r', encoding='utf-8') as file:
params = yaml.safe_load(file)