使用python编写帝王蝶优化算法
时间: 2023-07-05 16:14:01 浏览: 114
帝王蝶优化算法(Monarch Butterfly Optimization Algorithm, MBO)是一种基于群体智能的优化算法,其灵感来源于帝王蝶的觅食行为。下面是使用Python编写的帝王蝶优化算法的代码实现:
```
import random
import numpy as np
# 定义目标函数
def objective_function(x):
return x**2
# 定义帝王蝶优化算法
def monarch_butterfly_optimization(n_iterations, n_butterflies, search_range):
# 初始化种群
butterflies = np.zeros((n_butterflies, search_range.shape[0]))
for i in range(n_butterflies):
butterflies[i] = search_range[:, 0] + np.random.rand(search_range.shape[0]) * (search_range[:, 1] - search_range[:, 0])
# 初始化目标函数值
objective_values = np.zeros(n_butterflies)
for i in range(n_butterflies):
objective_values[i] = objective_function(butterflies[i])
# 寻找最优解
best_objective_value = np.min(objective_values)
best_solution = butterflies[np.argmin(objective_values)]
# 迭代寻优
for iteration in range(n_iterations):
# 更新每只蝴蝶的位置
for i in range(n_butterflies):
# 计算蝴蝶的速度
velocity = np.zeros(search_range.shape[0])
for j in range(search_range.shape[0]):
velocity[j] = random.uniform(-1, 1) * (best_solution[j] - butterflies[i][j])
# 更新蝴蝶的位置
new_position = butterflies[i] + velocity
# 限制位置在搜索范围内
for j in range(search_range.shape[0]):
if new_position[j] < search_range[j][0]:
new_position[j] = search_range[j][0]
if new_position[j] > search_range[j][1]:
new_position[j] = search_range[j][1]
# 更新目标函数值
new_objective_value = objective_function(new_position)
if new_objective_value < objective_values[i]:
butterflies[i] = new_position
objective_values[i] = new_objective_value
# 更新最优解
if new_objective_value < best_objective_value:
best_solution = new_position
best_objective_value = new_objective_value
return best_solution, best_objective_value
```
参数说明:
- n_iterations:迭代次数
- n_butterflies:蝴蝶数量
- search_range:搜索范围
使用示例:
```
search_range = np.array([[-5, 5]]) # 搜索范围
best_solution, best_objective_value = monarch_butterfly_optimization(n_iterations=100, n_butterflies=50, search_range=search_range)
print("最优解:", best_solution)
print("最优目标函数值:", best_objective_value)
```
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