沈静远-2023202210156-高级算法

沈静远-2023202210156-高级算法/VRPTW（源码）/GA.py

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+import random
+import matplotlib.pyplot as plt
+import numpy as np
+from collections import defaultdict, deque
+from deap import base, creator, tools, algorithms
+
+# 假设设置
+centersNum = 1  # 配送中心数量
+customerNum = 5  # 卸货点数量
+ordersMax = 5  # 每个时间段最大订单数
+timeInterval = 60  # 时间间隔（分钟）
+deliveryMax = 20  # 无人机飞行距离限制（公里）
+loadMax = 3  # 无人机最大负载（物品数）
+speed = 60  # 无人机速度（公里/小时）
+
+# 遗传算法设置
+POP_SIZE = 50  # 种群大小
+GEN = 30  # 迭代代数
+CX_PB = 0.7  # 交叉概率
+MUT_PB = 0.2  # 变异概率
+
+
+# 生成随机位置
+def generatePosition(count, size1, size2):
+    return [(random.randint(size1, size2), random.randint(size1, size2)) for _ in range(count)]
+
+# 位置距离计算
+def distance(pos1, pos2):
+    return ((pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2) ** 0.5
+
+# 生成的位置
+centers = generatePosition(centersNum, 3, 7)
+customers = generatePosition(customerNum, 0, 10)
+
+
+# 订单类
+class Order:
+    def __init__(self, priority, customerId, generateTime):
+        self.priority = priority  # 1: 一般, 2: 较紧急, 3: 紧急
+        self.customerId = customerId
+        self.generateTime = generateTime
+        self.deadline = self.setDeadline(generateTime, priority)
+        self.processed = False
+
+    def setDeadline(self, generateTime, priority):
+        if priority == 1:
+            return generateTime + 180  # 一般，3小时内送达
+        elif priority == 2:
+            return generateTime + 90  # 较紧急，1.5小时内送达
+        else:
+            return generateTime + 30  # 紧急，0.5小时内送达
+
+# 生成订单
+def generateOrders(time):
+    orderNum = random.randint(0, ordersMax)
+    return [Order(random.randint(1, 3), random.randint(0, customerNum - 1), time) for _
+            in range(orderNum)]
+
+def createRoute(individual, center, customers):
+    # 根据遗传算法个体生成路径
+    route = [center] + [customers[i] for i in individual] + [center]
+    return route
+
+def evalRoute(individual, center, customers):
+    # 计算路径的总距离
+    route = createRoute(individual, center, customers)
+    total_distance = sum(distance(route[i], route[i + 1]) for i in range(len(route) - 1))
+    return (total_distance,)
+
+def setupGA(center, customerIds, customers):
+    creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
+    creator.create("Individual", list, fitness=creator.FitnessMin)
+
+    toolbox = base.Toolbox()
+    # 确保随机抽样的元素数量不超过列表大小
+    sample_size = min(len(customerIds), len(customerIds))
+    toolbox.register("indices", random.sample, range(len(customerIds)), sample_size)
+    toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices)
+    toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+    toolbox.register("evaluate", evalRoute, center=center, customers=customers)
+    toolbox.register("mate", tools.cxOrdered)
+    toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
+    toolbox.register("select", tools.selTournament, tournsize=3)
+
+    return toolbox
+
+def optimizeRoutes(center, orders, customers):
+    customerIds = [order.customerId for order in orders if not order.processed]
+    if len(customerIds) < 2:  # 如果小于2个订单，则无法进行有效的路径规划
+        return [center] + [customers[idx] for idx in customerIds] + [center]
+
+    toolbox = setupGA(center, customerIds, customers)
+    pop = toolbox.population(n=POP_SIZE)
+    hof = tools.HallOfFame(1, similar=np.array_equal)
+
+    stats = tools.Statistics(lambda ind: ind.fitness.values)
+    stats.register("min", np.min)
+    stats.register("avg", np.mean)
+
+    algorithms.eaSimple(pop, toolbox, cxpb=CX_PB, mutpb=MUT_PB, ngen=GEN, stats=stats, halloffame=hof, verbose=False)
+
+    bestRoute = createRoute(hof[0], center, customers)
+    for idx in hof[0]:
+        orders[idx].processed = True
+    return bestRoute
+
+
+def selectDrones(orders, centers, customers):
+    paths = []
+    orders_by_center = defaultdict(list)
+
+    for order in orders:
+        if order.processed:
+            continue
+        customerPos = customers[order.customerId]
+        nearest_center = min(centers, key=lambda x: distance(x, customerPos))
+        orders_by_center[nearest_center].append(order)
+
+    for center, center_orders in orders_by_center.items():
+        if center_orders:
+            best_path = optimizeRoutes(center, center_orders, customers)
+            paths.append(best_path)
+    return paths
+
+# 绘图
+def plot(centers, customers, paths):
+    plt.figure(figsize=(10, 8))
+    colors = plt.cm.viridis(np.linspace(0, 1, len(paths)))
+
+    center_plotted, drop_plotted = False, False
+
+    for x, y in centers:
+        if not center_plotted:
+            plt.scatter(x, y, c='blue', alpha=1, marker=',', linewidths=3)
+            center_plotted = True
+        else:
+            plt.scatter(x, y, c='blue', alpha=1, marker=',', linewidths=3)
+
+    for x, y in customers:
+        if not drop_plotted:
+            plt.scatter(x, y, c='red', alpha=1, marker='o', linewidths=3)
+            drop_plotted = True
+        else:
+            plt.scatter(x, y, c='red', alpha=1, marker='o', linewidths=3)
+
+    if paths:  # 确保路径列表不为空
+        for idx, path in enumerate(paths):
+            # 期望路径格式：路径是一个包含起点、途径点和终点的列表
+            print(f"Path {idx + 1}: {path}")
+            plt.plot([p[0] for p in path], [p[1] for p in path],
+                     color=colors[idx], linestyle='--')
+
+    plt.legend(loc='best')
+    plt.title('Drone Delivery Routing')
+    plt.grid(False)
+    plt.show()
+
+# 使用改进的路径选择逻辑
+def simulation(duration):
+    orders = []
+    time = 0
+    paths = []
+
+    while time < duration:
+        new_orders = generateOrders(time)
+        orders.extend(new_orders)
+
+        new_paths = selectDrones(orders, centers, customers)
+        paths.extend(new_paths)
+
+        plot(centers, customers, paths)
+        plt.pause(1)
+
+        time += timeInterval
+
+    plt.show()
+
+
+# 运行模拟
+simulation(240)