人工蜂群算法代码
时间: 2023-07-03 07:06:06 浏览: 95
以下是一个简单的 Python 实现人工蜂群算法的示例代码:
```python
import random
import math
class Bee:
def __init__(self, pos, func):
self.pos = pos
self.func = func
self.value = self.func(self.pos)
class ABC:
def __init__(self, func, n_bees=10, n_iter=100):
self.func = func
self.n_bees = n_bees
self.n_iter = n_iter
self.bees = []
self.best_bee = None
self.__generate_bees()
def __generate_bees(self):
for i in range(self.n_bees):
pos = [random.uniform(-10, 10) for i in range(self.func.__code__.co_argcount)]
bee = Bee(pos, self.func)
self.bees.append(bee)
if self.best_bee is None or bee.value < self.best_bee.value:
self.best_bee = bee
def __search_neighborhood(self, current_bee):
phi = random.uniform(-1, 1)
index = random.randint(0, self.n_bees - 1)
neighbor_bee = self.bees[index]
new_pos = [current_bee.pos[i] + phi * (current_bee.pos[i] - neighbor_bee.pos[i])
for i in range(len(current_bee.pos))]
return Bee(new_pos, self.func)
def optimize(self):
for i in range(self.n_iter):
for j in range(self.n_bees):
current_bee = self.bees[j]
neighbor_bee = self.__search_neighborhood(current_bee)
if neighbor_bee.value < current_bee.value:
current_bee = neighbor_bee
if current_bee.value < self.best_bee.value:
self.best_bee = current_bee
self.__update_bees()
def __update_bees(self):
values = [bee.value for bee in self.bees]
mean = sum(values) / len(values)
std_dev = math.sqrt(sum([(val - mean) ** 2 for val in values]) / len(values))
for bee in self.bees:
phi = random.uniform(-1, 1)
new_pos = [bee.pos[i] + phi * std_dev for i in range(len(bee.pos))]
bee.pos = new_pos
bee.value = bee.func(bee.pos)
if bee.value < self.best_bee.value:
self.best_bee = bee
if __name__ == "__main__":
def sphere_function(*args):
return sum([arg ** 2 for arg in args])
abc = ABC(sphere_function, n_bees=20, n_iter=50)
abc.optimize()
print("Best solution found: f({}) = {}".format(abc.best_bee.pos, abc.best_bee.value))
```
该代码实现了一个人工蜂群算法的类 "ABC",其中包含一个函数 "optimize()",该函数在给定的函数空间中执行优化。函数空间由函数 "func" 和其参数个数 "n_bees" 定义。在这个示例中,优化的目标是最小化球形函数。
该算法的优化过程分为两个主要步骤:
- 搜索邻域:每个工蜂都在其邻域内搜索一个新位置,并计算其价值。
- 更新工蜂:每个工蜂都在其当前位置和新位置之间进行随机跳跃,以帮助搜索空间的探索和利用。
最终的优化结果存储在 "best_bee" 中,包括最佳解决方案的位置和价值。
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