115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
import random
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import math
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import yaml
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import numpy as np
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from utils import if_valid_partition, GA_solver
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from itertools import product
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import json
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from tqdm import tqdm
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np.random.seed(42)
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random.seed(42)
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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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# ---------------------------
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# 需要修改的超参数
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# ---------------------------
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R = 3
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C = 3
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params_file = 'params_50_50_3'
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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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W = params['W']
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k = params['num_cars']
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flight_time_factor = params['flight_time_factor']
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comp_time_factor = params['comp_time_factor']
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trans_time_factor = params['trans_time_factor']
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car_time_factor = params['car_time_factor']
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bs_time_factor = params['bs_time_factor']
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flight_energy_factor = params['flight_energy_factor']
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comp_energy_factor = params['comp_energy_factor']
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trans_energy_factor = params['trans_energy_factor']
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battery_energy_capacity = params['battery_energy_capacity']
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# 定义数字列表
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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=R))
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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=C))
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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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total_iterations = len(row_cuts_set) * len(col_cuts_set)
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with tqdm(total=total_iterations, desc="Processing") as pbar:
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for row_cuts in row_cuts_set:
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for col_cuts in col_cuts_set:
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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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# 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(
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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(
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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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best_solution = current_solution
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best_row_boundaries = row_boundaries
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best_col_boundaries = col_boundaries
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# 将best_solution分解成每个车队的路径
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found_start_points_indices = []
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for i in range(len(best_solution)):
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if best_solution[i] in to_process_idx:
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found_start_points_indices.append(i)
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car_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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car_path = []
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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(rectrangle_idx - 1)
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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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# 输出最佳方案
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print("Best solution:", best_solution)
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print("Time:", best_T)
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print("Row boundaries:", best_row_boundaries)
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print("Col boundaries:", best_col_boundaries)
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print("Car Paths:", car_paths)
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output_data = {
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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/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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