利用蚁群算法求解最佳工艺路线请给代码实例

以下是一个简单的 Python 代码实例，用于演示如何使用蚁群算法求解最佳工艺路线问题：

import random

# 蚂蚁数量
ANT_COUNT = 50
# 迭代次数
ITERATION = 100
# 信息素重要程度
ALPHA = 1
# 启发函数重要程度
BETA = 5
# 信息素挥发速度
RHO = 0.5
# 初始信息素浓度
INIT_PHEROMONE = 1.0
# 最大信息素浓度
MAX_PHEROMONE = 100.0

# 工艺路线图，这里用邻接矩阵表示
graph = [
    [0, 2, 3, 1, 4],
    [2, 0, 1, 2, 3],
    [3, 1, 0, 4, 1],
    [1, 2, 4, 0, 2],
    [4, 3, 1, 2, 0]
]

# 初始化信息素浓度矩阵
pheromone = [[INIT_PHEROMONE] * len(graph) for _ in range(len(graph))]

# 计算启发函数值
def calculate_heuristic(from_node, to_node):
    return 1.0 / graph[from_node][to_node]

# 根据概率选择下一步
def select_next_node(ant, visited):
    current_node = ant[-1]
    denominator = 0.0
    for i in range(len(graph)):
        if i not in visited:
            denominator += pheromone[current_node][i] ** ALPHA * calculate_heuristic(current_node, i) ** BETA
    probabilities = []
    for i in range(len(graph)):
        if i not in visited:
            numerator = pheromone[current_node][i] ** ALPHA * calculate_heuristic(current_node, i) ** BETA
            probabilities.append(numerator / denominator)
        else:
            probabilities.append(0.0)
    next_node = probabilities.index(max(probabilities))
    return next_node

# 更新信息素浓度
def update_pheromone():
    for i in range(len(graph)):
        for j in range(len(graph)):
            pheromone[i][j] *= (1 - RHO)
            pheromone[i][j] = max(pheromone[i][j], 0.0001)

# 蚁群算法主函数
def ant_colony_optimization():
    global pheromone
    best_path = []
    best_distance = float('inf')
    for _ in range(ITERATION):
        ants = [[random.randint(0, len(graph) - 1)] for _ in range(ANT_COUNT)]
        for i in range(len(graph) - 1):
            for ant in ants:
                visited = set(ant)
                next_node = select_next_node(ant, visited)
                ant.append(next_node)
        distances = []
        for ant in ants:
            distance = sum([graph[ant[i]][ant[i+1]] for i in range(len(graph) - 1)])
            distances.append(distance)
            if distance < best_distance:
                best_distance = distance
                best_path = ant[:]
        for i in range(len(graph) - 1):
            for j in range(len(graph)):
                delta_pheromone = 0.0
                for ant in ants:
                    if (ant[i] == j and ant[i+1] == j):
                        delta_pheromone += 1.0 / graph[ant[i]][ant[i+1]]
                pheromone[i][j] = (1 - RHO) * pheromone[i][j] + delta_pheromone
        update_pheromone()
    return best_path, best_distance

# 测试代码
best_path, best_distance = ant_colony_optimization()
print('最佳工艺路线为:', best_path)
print('最短距离为:', best_distance)

注：该代码仅为演示用途，可能存在不足之处，如有需要请自行修改和完善。