模拟退火微调分割

GA/ga.py

@@ -150,7 +150,7 @@ class GA(object):
             merged_path = first['car_path'] + second['car_path']
             merged_time = first['car_time'] + second['car_time']
             merged_dict = {'car_path': merged_path, 'car_time': merged_time}
-            sorted_car_infos = [merged_dict] + sorted_car_infos[3:]
+            sorted_car_infos = [merged_dict] + sorted_car_infos[2:]
 
         # 计算各系统的总时间max(飞行时间+车的时间, 机巢计算时间)
         T_k_list = []

GA/main.py

@@ -85,8 +85,7 @@ with tqdm(total=total_iterations, desc="Processing") as pbar:
                         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(
+                                car_path.append(rectrangle_idx - 1)
-                                    rectrangles[rectrangle_idx - 1]['center'])
                         if car_path:
                             car_paths.append(car_path)
                 pbar.update(1)

GA/main_parallel.py

@@ -83,7 +83,8 @@ if __name__ == "__main__":  # 重要：在 Windows 上必须加这一行
                         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
+                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):
@@ -113,8 +114,7 @@ if __name__ == "__main__":  # 重要：在 Windows 上必须加这一行
                     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(
+                            car_path.append(rectrangle_idx - 1)
-                                rectrangles[rectrangle_idx - 1]['center'])
                     if car_path:
                         car_paths.append(car_path)
 

GA/sa_finetune.py

@@ -0,0 +1,217 @@
+import random
+import math
+import json
+import yaml
+
+
+class SA_FineTuner:
+    def __init__(self, initial_row_cuts, initial_col_cuts, car_paths, max_iterations=1000, initial_temp=100, cooling_rate=0.95):
+        """
+        初始化模拟退火算法
+        :param initial_row_cuts: 初始行切分比例列表，例如 [0.1, 0.3, 0.7]
+        :param initial_col_cuts: 初始列切分比例列表，例如 [0.2, 0.5, 0.8]
+        :param T_function: 目标函数，用于计算切分比例的目标值 T
+        :param max_iterations: 最大迭代次数
+        :param initial_temp: 初始温度
+        :param cooling_rate: 温度下降速率
+        """
+        # 读取参数
+        with open('params3.yml', 'r', encoding='utf-8') as file:
+            params = yaml.safe_load(file)
+
+        self.H = params['H']
+        self.W = params['W']
+        self.num_cars = params['num_cars']
+
+        self.flight_time_factor = params['flight_time_factor']
+        self.comp_time_factor = params['comp_time_factor']
+        self.trans_time_factor = params['trans_time_factor']
+        self.car_time_factor = params['car_time_factor']
+        self.bs_time_factor = params['bs_time_factor']
+
+        self.flight_energy_factor = params['flight_energy_factor']
+        self.comp_energy_factor = params['comp_energy_factor']
+        self.trans_energy_factor = params['trans_energy_factor']
+        self.battery_energy_capacity = params['battery_energy_capacity']
+
+        self.current_row_cuts = initial_row_cuts[:]
+        self.current_col_cuts = initial_col_cuts[:]
+        self.car_paths = car_paths
+        self.current_T = self.T_function(
+            self.current_row_cuts, self.current_col_cuts)  # 初始目标值
+
+        self.best_row_cuts = self.current_row_cuts[:]
+        self.best_col_cuts = self.current_col_cuts[:]
+        self.best_T = self.current_T
+
+        # 模拟退火相关参数
+        self.max_iterations = max_iterations
+        self.initial_temp = initial_temp
+        self.cooling_rate = cooling_rate
+        self.temperature = initial_temp
+
+    def fine_tune(self, cuts):
+        """
+        微调切分比例
+        :param cuts: 当前切分比例列表，例如 [0.1, 0.3, 0.7]
+        :return: 微调后的切分比例列表
+        """
+        new_cuts = cuts[:]
+        if not new_cuts:
+            return new_cuts
+
+        # 随机选择一个切分点进行微调
+        index = random.randint(1, len(new_cuts) - 2)    # 左闭右闭！！！
+        adjustment = random.choice([-0.01, 0.01])  # 固定步长调整
+        new_cuts[index] += adjustment
+
+        # 确保切分比例合法
+        if new_cuts[index] >= new_cuts[index + 1]:
+            new_cuts[index] = new_cuts[index + 1] - 0.01
+        elif new_cuts[index] <= new_cuts[index - 1]:
+            new_cuts[index] = new_cuts[index - 1] + 0.01
+        else:
+            pass
+        new_cuts[index] = max(0.01, min(0.99, new_cuts[index]))
+
+        return new_cuts
+
+    def accept_solution(self, current_T, new_T, temperature):
+        """
+        判断是否接受新解
+        :param current_T: 当前解的目标值
+        :param new_T: 新解的目标值
+        :param temperature: 当前温度
+        :return: 是否接受新解（True/False）
+        """
+        if new_T < current_T:
+            return True
+        else:
+            # 根据模拟退火的概率公式接受较差解
+            delta = new_T - current_T
+            acceptance_probability = math.exp(-delta / temperature)
+            return random.random() < acceptance_probability
+
+    def T_function(self, row_cuts, col_cuts):
+        """
+        计算切分比例的目标值 T（占位函数）
+        :param row_cuts: 行切分比例
+        :param col_cuts: 列切分比例
+        :return: 目标值 T
+        """
+        rectangles = []
+        for i in range(len(row_cuts) - 1):
+            for j in range(len(col_cuts) - 1):
+                d = (col_cuts[j+1] - col_cuts[j]) * self.W * \
+                    (row_cuts[i+1] - row_cuts[i]) * self.H
+                rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \
+                    (self.comp_time_factor - self.trans_time_factor)
+                rho_energy_limit = (self.battery_energy_capacity - self.flight_energy_factor * d - self.trans_energy_factor * d) / \
+                    (self.comp_energy_factor * d -
+                        self.trans_energy_factor * d)
+                if rho_energy_limit < 0:
+                    return float('inf')
+                rho = min(rho_time_limit, rho_energy_limit)
+
+                flight_time = self.flight_time_factor * d
+                bs_time = self.bs_time_factor * (1 - rho) * d
+
+                rectangles.append({
+                    'flight_time': flight_time,
+                    'bs_time': bs_time,
+                    'center': ((row_cuts[i] + row_cuts[i+1]) / 2.0 * self.H,
+                               (col_cuts[j] + col_cuts[j+1]) / 2.0 * self.W)
+                })
+
+        mortorcade_time_lt = []
+        for idx in range(self.num_cars):
+            car_path = self.car_paths[idx]
+
+            flight_time = sum(rectangles[point]['flight_time']
+                              for point in car_path)
+            bs_time = sum(rectangles[point]['bs_time'] for point in car_path)
+
+            car_time = 0
+            for i in range(len(car_path) - 1):
+                first_point = car_path[i]
+                second_point = car_path[i + 1]
+                car_time += math.dist(
+                    rectangles[first_point]['center'], rectangles[second_point]['center']) * self.car_time_factor
+            car_time += math.dist(rectangles[0]['center'],
+                                  [self.H / 2, self.W / 2]) * self.car_time_factor
+            car_time += math.dist(rectangles[-1]['center'],
+                                  [self.H / 2, self.W / 2]) * self.car_time_factor
+            mortorcade_time_lt.append(max(car_time + flight_time, bs_time))
+
+        return max(mortorcade_time_lt)
+
+    def run(self):
+        """
+        运行模拟退火算法
+        :return: 最优行切分比例、最优列切分比例、最小目标值 T
+        """
+        for iteration in range(self.max_iterations):
+            # 随机选择行或列进行微调
+            if random.random() < 0.5:
+                new_row_cuts = self.fine_tune(self.current_row_cuts)
+                new_col_cuts = self.current_col_cuts[:]
+            else:
+                new_row_cuts = self.current_row_cuts[:]
+                new_col_cuts = self.fine_tune(self.current_col_cuts)
+
+            # 计算新解的目标值
+            new_T = self.T_function(new_row_cuts, new_col_cuts)
+
+            # 判断是否接受新解
+            if self.accept_solution(self.current_T, new_T, self.temperature):
+                self.current_row_cuts = new_row_cuts
+                self.current_col_cuts = new_col_cuts
+                self.current_T = new_T
+
+                # 更新最优解
+                if new_T < self.best_T:
+                    self.best_row_cuts = new_row_cuts
+                    self.best_col_cuts = new_col_cuts
+                    self.best_T = new_T
+
+            # 降低温度
+            self.temperature *= self.cooling_rate
+
+            # 打印进度（可选）
+            if iteration % 100 == 0:
+                print(
+                    f"Iteration {iteration}: Best T = {self.best_T}, Temperature = {self.temperature}")
+
+        return self.best_row_cuts, self.best_col_cuts, self.best_T
+
+
+def load_initial_solution(file_path):
+    """
+    从 JSON 文件加载初始解
+    :param file_path: JSON 文件路径
+    :return: 行切分比例、列切分比例
+    """
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    row_cuts = data['row_boundaries']
+    col_cuts = data['col_boundaries']
+    car_paths = data['car_paths']
+    return row_cuts, col_cuts, car_paths
+
+
+# 示例调用
+if __name__ == "__main__":
+    random.seed(42)
+
+    # 从 JSON 文件加载初始解
+    file_path = r"e:\studio2\HPCC_code\solutions\trav_ga_params3_parallel.json"
+    initial_row_cuts, initial_col_cuts, car_paths = load_initial_solution(
+        file_path)
+
+    sa = SA_FineTuner(initial_row_cuts, initial_col_cuts, car_paths)
+    best_row_cuts, best_col_cuts, best_T = sa.run()
+
+    # 输出结果
+    print("Best row cuts:", best_row_cuts)
+    print("Best col cuts:", best_col_cuts)
+    print("Best T:", best_T)

mtkl_sovler.py

@@ -12,8 +12,8 @@ num_iterations = 10000
 # ---------------------------
 # 参数设置
 # ---------------------------
-params_file = 'params2.yml'
+params_file = 'params3'
-with open(params_file, 'r', encoding='utf-8') as file:
+with open(params_file + '.yml', 'r', encoding='utf-8') as file:
     params = yaml.safe_load(file)
 
 H = params['H']
@@ -39,8 +39,10 @@ best_solution = None
 
 for iteration in range(num_iterations):
     # 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
+    # R = random.randint(0, 3)  # 行分段数
-    C = random.randint(0, 3)  # 列分段数
+    # C = random.randint(0, 3)  # 列分段数
+    R = 1
+    C = 1
 
     # 生成随机的行、列分割边界
     horiz = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(R)]

solutions/mtkl_params3.json

@@ -0,0 +1,20 @@
+{
+    "row_boundaries": [
+        0.0,
+        0.5,
+        1.0
+    ],
+    "col_boundaries": [
+        0.0,
+        0.5,
+        1.0
+    ],
+    "car_paths": [
+        [
+            0, 2
+        ],
+        [
+            1, 3
+        ]
+    ]
+}

solutions/trav_ga_params3.json

@@ -0,0 +1,34 @@
+{
+    "row_boundaries": [
+        0.0,
+        0.3,
+        1.0
+    ],
+    "col_boundaries": [
+        0.0,
+        0.4,
+        1.0
+    ],
+    "car_paths": [
+        [
+            [
+                19.5,
+                21.0
+            ]
+        ],
+        [
+            [
+                4.5,
+                6.0
+            ],
+            [
+                4.5,
+                21.0
+            ],
+            [
+                19.5,
+                6.0
+            ]
+        ]
+    ]
+}

solutions/trav_ga_params3_parallel.json

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