果蝇优化算法python
时间: 2023-10-13 21:06:09 浏览: 63
果蝇优化算法(Fruit Fly Optimization Algorithm,简称FOA)是一种基于自然界果蝇觅食行为的启发式优化算法。它模拟了果蝇在寻找食物时的行为,通过不断地调整果蝇的位置和方向来寻找最优解。FOA算法具有全局搜索能力强、收敛速度快等优点,在解决复杂优化问题方面具有广泛的应用。
以下是FOA算法的Python实现示例:
```python
import random
import math
# 目标函数
def func(x):
return x * math.sin(10 * math.pi * x) + 2.0
# FOA算法
def FOA(max_iter, pop_size, dim, lb, ub):
# 初始化果蝇群体
flies = [[random.uniform(lb, ub) for i in range(dim)] for j in range(pop_size)]
# 初始化果蝇最优位置和最优适应度值
best_pos = [0.0] * dim
best_fit = float('-inf')
# 迭代寻优
for t in range(max_iter):
# 计算果蝇适应度值
fits = [func(fly) for fly in flies]
# 更新果蝇最优位置和最优适应度值
for i in range(pop_size):
if fits[i] > best_fit:
best_fit = fits[i]
best_pos = flies[i]
# 计算果蝇权重
w = [fits[i] / sum(fits) for i in range(pop_size)]
# 更新果蝇位置
for i in range(pop_size):
for j in range(dim):
r = random.random()
if r < w[i]:
flies[i][j] = best_pos[j] + random.uniform(-1, 1) * abs(best_pos[j] - flies[i][j])
else:
k = random.randint(0, pop_size - 1)
flies[i][j] = flies[k][j]
return best_pos, best_fit
# 测试
best_pos, best_fit = FOA(max_iter=100, pop_size=50, dim=1, lb=-1, ub=2)
print('best position:', best_pos)
print('best fitness:', best_fit)
```