蚁群算法实验python背包问题

以下是使用蚁群算法解决0/1背包问题的Python实现：

import random

# 蚂蚁数量
ant_count = 50
# 迭代次数
iterations = 100
# 信息素挥发系数
rho = 0.1
# 信息素强度
Q = 1
# 启发因子
alpha = 1
# 期望因子
beta = 2
# 背包容量
capacity = 50
# 物品数量
item_count = 20
# 物品重量
weights = [random.randint(1, 10) for _ in range(item_count)]
# 物品价值
values = [random.randint(1, 10) for _ in range(item_count)]
# 信息素矩阵
pheromone = [[1.0 for _ in range(item_count)] for _ in range(ant_count)]
# 最优解
best_solution = []
# 最优解的价值
best_value = 0


# 计算蚂蚁i选择物品j的概率
def calculate_probabilities(i, j, solutions):
    # 计算分母
    denominator = 0.0
    for k in range(item_count):
        if k not in solutions[i]:
            denominator += pow(pheromone[i][k], alpha) * pow(1.0 / weights[k], beta)
    # 计算概率
    probabilities = []
    for k in range(item_count):
        if k in solutions[i]:
            probabilities.append(0.0)
        else:
            numerator = pow(pheromone[i][k], alpha) * pow(1.0 / weights[k], beta)
            probabilities.append(numerator / denominator)
    return probabilities


# 选择物品
def select_item(i, solutions):
    probabilities = calculate_probabilities(i, 0, solutions)
    r = random.random()
    s = 0.0
    for k in range(item_count):
        s += probabilities[k]
        if s >= r:
            return k


# 更新信息素
def update_pheromone(solutions):
    global pheromone
    # 信息素挥发
    for i in range(ant_count):
        for j in range(item_count):
            pheromone[i][j] *= (1.0 - rho)
    # 信息素更新
    for i in range(ant_count):
        solution = solutions[i]
        value = 0
        for j in range(item_count):
            if j in solution:
                pheromone[i][j] += Q / value
                value += values[j]


# 蚁群算法
def ant_colony():
    global best_solution, best_value
    # 迭代
    for t in range(iterations):
        # 每只蚂蚁的解
        solutions = [[] for _ in range(ant_count)]
        # 每只蚂蚁选择物品
        for i in range(ant_count):
            solution = solutions[i]
            weight = 0
            while weight < capacity:
                item = select_item(i, solutions)
                if weight + weights[item] <= capacity:
                    solution.append(item)
                    weight += weights[item]
            # 计算解的价值
            value = 0
            for item in solution:
                value += values[item]
            # 更新最优解
            if value > best_value:
                best_solution = solution
                best_value = value
        # 更新信息素
        update_pheromone(solutions)


# 运行蚁群算法
ant_colony()

# 输出结果
print("最优解：", best_solution)
print("最优解的价值：", best_value)