133 lines
4.7 KiB
Python
133 lines
4.7 KiB
Python
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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
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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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from concurrent.futures import ProcessPoolExecutor, as_completed
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from GA_MTSP import MTSP_GA
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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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rectangles = if_valid_partition(row_boundaries, col_boundaries, params)
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if not rectangles:
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return None # 过滤无效划分
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cities = [(params['H'] / 2.0, params['W'] / 2.0)]
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for rec in rectangles:
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cities.append(rec['center'])
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sovler = MTSP_GA(
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cities=cities, params=params, rectangles=rectangles, population_size=200, max_iterations=2000)
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current_time, current_solution = sovler.solve()
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return (current_solution, current_time, row_boundaries, col_boundaries, rectangles)
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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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# ---------------------------
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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 = 1
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params_file = 'params_50_50_3'
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batch_size = 60 # 控制一次最多并行多少个任务
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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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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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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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futures = set()
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results = []
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with tqdm(total=total_iterations) as pbar:
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for task in all_tasks:
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if len(futures) >= batch_size: # 如果并行任务数达到 batch_size，等待已有任务完成
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for future in as_completed(futures):
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results.append(future.result())
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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(
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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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results.append(future.result())
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pbar.update(1)
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# 处理计算结果，找到最优解
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for result in results:
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if result:
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current_solution, current_time, row_boundaries, col_boundaries, rectangles = result
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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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# 解析最佳路径
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car_paths = [[x-1 for x in sublist]
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for sublist in best_solution]
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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}_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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