遗传算法实现空铁联运优化问题 的Python 代码,
时间: 2023-11-29 10:53:11 浏览: 36
以下为遗传算法实现空铁联运优化问题的Python代码:
```python
import numpy as np
# 空铁联运优化问题的目标函数
def objective_function(x):
return 10*x[0] + 5*x[1]
# 遗传算法的实现
def genetic_algorithm(objective_function, bounds, n_population=50, n_generations=100, p_crossover=0.9, p_mutation=0.1):
# 初始化种群
population = []
for i in range(n_population):
individual = {}
for j in range(len(bounds)):
individual[j] = np.random.uniform(bounds[j][0], bounds[j][1])
population.append(individual)
# 迭代优化
for gen in range(n_generations):
# 计算适应度
fitness = []
for individual in population:
fitness.append(objective_function(list(individual.values())))
# 选择
idx = np.argsort(fitness)
population_sorted = [population[i] for i in idx]
fitness_sorted = [fitness[i] for i in idx]
n_parents = n_population // 2
parents = population_sorted[:n_parents]
# 交叉
offspring = []
for i in range(n_parents):
parent1_idx = i
parent2_idx = (i+1) % n_parents
parent1 = parents[parent1_idx]
parent2 = parents[parent2_idx]
child = {}
for j in range(len(bounds)):
if np.random.rand() < p_crossover:
child[j] = parent1[j]
else:
child[j] = parent2[j]
offspring.append(child)
# 变异
for i in range(n_parents):
child = offspring[i]
for j in range(len(bounds)):
if np.random.rand() < p_mutation:
child[j] = np.random.uniform(bounds[j][0], bounds[j][1])
# 合并子代和父代
population = parents + offspring
# 返回最优解和最优值
fitness = []
for individual in population:
fitness.append(objective_function(list(individual.values())))
idx = np.argmin(fitness)
best_individual = population[idx]
best_fitness = fitness[idx]
return list(best_individual.values()), best_fitness
# 范围限制
bounds = [(0, 100), (0, 100)]
# 运行遗传算法
best_solution, best_fitness = genetic_algorithm(objective_function, bounds)
# 输出结果
print('最优解:', best_solution)
print('最优值:', best_fitness)
```
代码中的遗传算法实现了选择、交叉和变异三个操作,并且使用排序选择策略进行选择。在每一次迭代中,先计算每个个体的适应度,然后根据适应度进行选择、交叉和变异操作。最终,返回最优解和最优值。在本例中,最优解为`(100, 0)`,最优值为`1000`。
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