HPCC2025/GA/main_parallel.py

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
from concurrent.futures import ProcessPoolExecutor, as_completed


def process_partition(row_cuts, col_cuts, params):
    row_boundaries = [0.0] + list(row_cuts) + [1.0]
    col_boundaries = [0.0] + list(col_cuts) + [1.0]
    rectrangles = if_valid_partition(row_boundaries, col_boundaries, params)

    if not rectrangles:
        return None  # 过滤无效划分

    current_solution, current_time, to_process_idx = GA_solver(
        rectrangles, params)

    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
    np.random.seed(42)
    random.seed(42)

    # ---------------------------
    # 需要修改的超参数
    # ---------------------------
    R = 1
    C = 1
    params_file = 'params3'
    batch_size = 10  # 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)

    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=R))
    # 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=C))
    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_solution = None
    best_row_boundaries = None
    best_col_boundaries = None

    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)

    with ProcessPoolExecutor(max_workers=batch_size) as executor:
        futures = set()
        results = []

        with tqdm(total=total_iterations) as pbar:
            for task in all_tasks:
                if len(futures) >= batch_size:  # 如果并行任务数达到 batch_size，等待已有任务完成
                    for future in as_completed(futures):
                        results.append(future.result())
                        pbar.update(1)  # 更新进度条
                    futures.clear()  # 清空已完成的任务

                futures.add(executor.submit(
                    process_partition, *task, params))  # 提交新任务

            # 处理剩余未完成的任务
            for future in as_completed(futures):
                results.append(future.result())
                pbar.update(1)

    # 处理计算结果，找到最优解
    for result in results:
        if result:
            current_solution, current_time, row_boundaries, col_boundaries, to_process_idx, rectrangles = result
            if current_time < best_T:
                best_T = current_time
                best_solution = current_solution
                best_row_boundaries = row_boundaries
                best_col_boundaries = col_boundaries

                # 解析最佳路径
                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]
                        if rectrangle_idx not in to_process_idx:
                            car_path.append(rectrangle_idx - 1)
                    if car_path:
                        car_paths.append(car_path)

    # 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)
    print("Car Paths:", car_paths)

    output_data = {
        'row_boundaries': best_row_boundaries,
        'col_boundaries': best_col_boundaries,
        'car_paths': car_paths
    }
    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)