并行遗传算法的python实现
时间: 2023-09-09 08:12:03 浏览: 95
并行遗传算法是一种基于遗传算法的优化方法,通过将遗传算法的计算任务分解为多个子任务并行进行计算,以提高算法的效率和准确性。下面是一个基于Python实现的并行遗传算法示例:
```python
import numpy as np
import multiprocessing as mp
# 目标函数
def fitness_func(x):
return np.sum(x ** 2)
# 个体编码
def encode():
return np.random.rand(10)
# 交叉操作
def crossover(x1, x2):
mask = np.random.rand(len(x1)) < 0.5
x_new = np.copy(x1)
x_new[mask] = x2[mask]
return x_new
# 变异操作
def mutate(x, mutate_prob):
mask = np.random.rand(len(x)) < mutate_prob
x_new = np.copy(x)
x_new[mask] += np.random.normal(size=np.sum(mask))
return x_new
# 遗传算法主函数
def ga_worker(task_queue, result_queue):
while True:
task = task_queue.get()
if task is None:
break
pop_size, n_generations, elite_size, crossover_prob, mutate_prob = task
pop = [encode() for _ in range(pop_size)]
for i in range(n_generations):
# 计算适应度
fitness = [fitness_func(x) for x in pop]
# 选择精英个体
elite_idx = np.argsort(fitness)[:elite_size]
elite_pop = [pop[idx] for idx in elite_idx]
# 交叉操作
offspring_pop = []
while len(offspring_pop) < pop_size - elite_size:
idx1, idx2 = np.random.choice(len(elite_pop), size=2, replace=False)
if np.random.rand() < crossover_prob:
offspring_pop.append(crossover(elite_pop[idx1], elite_pop[idx2]))
# 变异操作
offspring_pop = [mutate(x, mutate_prob) for x in offspring_pop]
# 合并精英个体和后代个体
pop = elite_pop + offspring_pop
# 计算最终适应度
fitness = [fitness_func(x) for x in pop]
result_queue.put(np.min(fitness))
if __name__ == '__main__':
# 设置参数
pop_size = 100
n_generations = 100
elite_size = 10
crossover_prob = 0.8
mutate_prob = 0.1
n_workers = mp.cpu_count()
# 创建任务队列和结果队列
task_queue = mp.Queue()
result_queue = mp.Queue()
# 启动工作进程
workers = []
for _ in range(n_workers):
worker = mp.Process(target=ga_worker, args=(task_queue, result_queue))
worker.start()
workers.append(worker)
# 提交任务
for i in range(10):
task_queue.put((pop_size, n_generations, elite_size, crossover_prob, mutate_prob))
# 获取结果
for i in range(10):
result = result_queue.get()
print('Iteration {}, best fitness: {:.4f}'.format(i, result))
# 结束工作进程
for _ in range(n_workers):
task_queue.put(None)
for worker in workers:
worker.join()
```
在该示例中,我们首先定义了目标函数、个体编码、交叉操作和变异操作等函数,然后定义了一个遗传算法主函数`ga_worker`,并将其作为工作进程启动。在主函数中,我们使用多个参数来控制遗传算法的执行,例如种群大小、进化代数、精英个体数、交叉概率和变异概率等。我们通过任务队列和结果队列来分别提交任务和获取结果,并使用多个工作进程来并行执行遗传算法。最后,我们在主进程中打印每次迭代的最佳适应度。
注意,该示例仅是一个简单的示例,实际应用中还需要根据具体问题进行参数调整和算法优化。