请为多式联运路径优化设计一段代码
时间: 2024-05-04 12:18:38 浏览: 19
以下是一个基于遗传算法的多式联运路径优化的示例代码:
```python
import random
# 初始化参数
POPULATION_SIZE = 50
GENERATION_COUNT = 100
MUTATION_RATE = 0.01
CROSSOVER_RATE = 0.8
SELECTION_RATE = 0.2
# 定义一个路径类
class Path:
def __init__(self, genes):
self.genes = genes
self.distance = 0
# 计算路径距离
def calculate_distance(self):
self.distance = 0
for i in range(len(self.genes) - 1):
self.distance += distance_matrix[self.genes[i]][self.genes[i + 1]]
# 交换两个基因的位置
def swap_genes(self, i, j):
self.genes[i], self.genes[j] = self.genes[j], self.genes[i]
self.distance = 0
# 基因变异
def mutate(self):
for i in range(len(self.genes)):
if random.random() < MUTATION_RATE:
j = random.randint(0, len(self.genes) - 1)
self.swap_genes(i, j)
# 初始化距离矩阵
distance_matrix = [
[0, 10, 20, 30, 40],
[10, 0, 15, 25, 35],
[20, 15, 0, 14, 28],
[30, 25, 14, 0, 12],
[40, 35, 28, 12, 0]
]
# 初始化种群
population = []
for i in range(POPULATION_SIZE):
genes = [0, 1, 2, 3, 4]
random.shuffle(genes)
path = Path(genes)
path.calculate_distance()
population.append(path)
# 进化过程
for generation in range(GENERATION_COUNT):
# 选择操作
population.sort(key=lambda path: path.distance)
selected_population = population[:int(SELECTION_RATE * POPULATION_SIZE)]
# 交叉操作
for i in range(int(CROSSOVER_RATE * POPULATION_SIZE)):
parent1, parent2 = random.sample(selected_population, 2)
child_genes = parent1.genes[:]
for j in range(len(parent2.genes)):
if parent2.genes[j] not in child_genes:
index = random.randint(0, len(child_genes) - 1)
child_genes[index] = parent2.genes[j]
child = Path(child_genes)
child.calculate_distance()
population.append(child)
# 变异操作
for path in population:
path.mutate()
path.calculate_distance()
# 更新种群
population.sort(key=lambda path: path.distance)
population = population[:POPULATION_SIZE]
# 输出最优解
best_path = population[0]
print("Best path:", best_path.genes, "Distance:", best_path.distance)
```
这段代码实现了一个基于遗传算法的多式联运路径优化算法。其中,距离矩阵表示了各个节点之间的距离,种群中每个个体都是一条路径,遗传算法通过选择、交叉和变异操作来搜索最优解。最终,输出种群中距离最短的路径作为最优解。
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