车间布局优化算法实现代码
时间: 2023-10-11 10:08:33 浏览: 61
车间布局优化算法的具体实现代码因算法的选择和具体问题的不同而有所差异。下面以遗传算法为例,给出一个简化的车间布局优化算法的实现代码:
```python
import random
# 车间布局优化问题的参数和约束条件
num_devices = 10 # 设备数量
device_sizes = [2, 3, 4, 1, 2, 3, 1, 2, 1, 3] # 设备的尺寸
max_distance = 100 # 最大移动距离
population_size = 50 # 种群大小
num_generations = 100 # 迭代次数
# 初始化种群
def init_population():
population = []
for _ in range(population_size):
layout = random.sample(range(num_devices), num_devices) # 随机生成一个布局方案
population.append(layout)
return population
# 计算适应度函数(评估布局方案好坏)
def fitness(layout):
total_distance = 0
for i in range(num_devices):
for j in range(i + 1, num_devices):
distance = abs(layout[i] - layout[j])
total_distance += distance * device_sizes[i] * device_sizes[j]
return total_distance
# 交叉操作
def crossover(parent1, parent2):
point = random.randint(0, num_devices - 1)
child = parent1[:point] + parent2[point:]
return child
# 变异操作
def mutate(layout):
point1, point2 = random.sample(range(num_devices), 2)
layout[point1], layout[point2] = layout[point2], layout[point1]
return layout
# 遗传算法主函数
def genetic_algorithm():
population = init_population()
for _ in range(num_generations):
# 计算适应度函数并排序
fitness_scores = [fitness(layout) for layout in population]
sorted_population = [layout for _, layout in sorted(zip(fitness_scores, population))]
# 选择操作(保留适应度较好的个体)
elite_size = int(population_size * 0.2)
elites = sorted_population[:elite_size]
# 交叉操作(生成新个体)
offspring = []
while len(offspring) < population_size - elite_size:
parent1, parent2 = random.sample(sorted_population, 2)
child = crossover(parent1, parent2)
offspring.append(child)
# 变异操作(引入新的基因变化)
offspring = [mutate(layout) for layout in offspring]
# 更新种群
population = elites + offspring
# 返回最优布局方案
best_layout = population[0]
return best_layout
# 执行遗传算法求解车间布局优化问题
best_layout = genetic_algorithm()
print("最优布局方案:", best_layout)
```
以上代码是一个简单的遗传算法实现,其中初始化种群、计算适应度函数、交叉操作和变异操作等是遗传算法中常用的操作。需要根据具体问题的参数和约束条件进行相应的调整和扩展。此外,还可以使用其他优化算法来求解车间布局优化问题,只需相应地修改代码中的优化算法部分即可。