101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
|
import random
|
|||
|
|
import math
|
|||
|
|
import yaml
|
|||
|
|
import numpy as np
|
|||
|
|
from utils import if_valid_partition, GA_solver
|
|||
|
|
from itertools import product
|
|||
|
|
import json
|
|||
|
|
from tqdm import tqdm
|
|||
|
|
|
|||
|
|
np.random.seed(42)
|
|||
|
|
random.seed(42)
|
|||
|
|
best_T = float('inf')
|
|||
|
|
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 = 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)
|
|||
|
|
|
|||
|
|
total_iterations = len(row_cuts_set) * len(col_cuts_set)
|
|||
|
|
with tqdm(total=total_iterations, desc="Processing") as pbar:
|
|||
|
|
for row_cuts in row_cuts_set:
|
|||
|
|
for col_cuts in col_cuts_set:
|
|||
|
|
row_boundaries = [0.0] + list(row_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！
|
|||
|
|
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)
|
|||
|
|
|
|||
|
|
if current_time < best_T:
|
|||
|
|
best_T = current_time
|
|||
|
|
best_solution = current_solution
|
|||
|
|
best_row_boundaries = row_boundaries
|
|||
|
|
best_col_boundaries = col_boundaries
|
|||
|
|
|
|||
|
|
# 将best_solution分解成每个车队的路径
|
|||
|
|
found_start_points_indices = []
|
|||
|
|
for i in range(len(best_solution)):
|
|||
|
|
if best_solution[i] in to_process_idx:
|
|||
|
|
found_start_points_indices.append(i)
|
|||
|
|
car_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]
|
|||
|
|
car_path = []
|
|||
|
|
for k in range(from_index, end_index + 1):
|
|||
|
|
rectrangle_idx = best_solution[k]
|
|||
|
|
car_path.append(rectrangles[rectrangle_idx]['center'])
|
|||
|
|
car_paths.append(car_path)
|
|||
|
|
pbar.update(1)
|
|||
|
|
|
|||
|
|
# 输出最佳方案
|
|||
|
|
print("Best solution:", best_solution)
|
|||
|
|
print("Time:", best_T)
|
|||
|
|
print("Row boundaries:", best_row_boundaries)
|
|||
|
|
print("Col boundaries:", best_col_boundaries)
|
|||
|
|
|
|||
|
|
output_data = {
|
|||
|
|
'row_boundaries': row_boundaries,
|
|||
|
|
'col_boundaries': 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)
|