python如何通过Pygmo库导入whale_optimization算法
时间: 2024-04-29 22:25:20 浏览: 23
您可以通过以下代码导入Pygmo库中的鲸鱼优化算法:
```python
import pygmo as pg
# 创建一个鲸鱼优化算法对象
algo = pg.algorithm(pg.whale_optimization)
# 运行算法
pop = pg.population(prob)
# 可以使用pop来获取优化结果
```
相关问题
白鲸优化算法python代码
以下是一个简单的白鲸优化算法的 Python 实现示例:
```python
import numpy as np
# 定义目标函数
def objective_function(x):
return np.sum(np.square(x))
# 定义白鲸优化算法
def whale_optimization_algorithm(obj_func, num_iterations=100, num_whales=10, search_space=(-100, 100), a=2):
# 初始化鲸群
search_min, search_max = search_space
dimensions = len(search_min)
whales = []
for i in range(num_whales):
whale = np.random.uniform(search_min, search_max, dimensions)
whales.append(whale)
# 开始优化
for t in range(num_iterations):
# 计算当前最优解
current_best_whale = None
current_best_score = None
for whale in whales:
score = obj_func(whale)
if current_best_score is None or score < current_best_score:
current_best_score = score
current_best_whale = whale
# 遍历所有鲸
for i, whale in enumerate(whales):
# 随机选择一个领袖鲸
leader_index = np.random.randint(num_whales)
leader = whales[leader_index]
# 计算距离和方向
distance = np.abs(leader - whale)
direction = leader - whale
# 更新位置
a = 2 - t * ((2) / num_iterations) # 动态线性降温参数
b = 1
l = np.random.uniform(-1, 1, dimensions)
p = np.random.uniform(0, 1, dimensions)
A = a * np.multiply(np.multiply(2 * b, p) - b, np.abs(a * current_best_whale - whale))
C = np.multiply(2 * l, distance)
new_whale = whale + A - C
# 边界处理
new_whale = np.clip(new_whale, search_min, search_max)
# 更新鲸
whales[i] = new_whale
return current_best_whale, current_best_score
```
使用示例:
```python
search_min = np.array([-5, -5, -5])
search_max = np.array([5, 5, 5])
best_whale, best_score = whale_optimization_algorithm(objective_function, num_iterations=100, num_whales=10, search_space=(search_min, search_max))
print('Best solution: ', best_whale)
print('Best objective function value: ', best_score)
```
注:以上代码仅作为示例,实际应用中可能需要根据具体情况进行调整和优化。
鲸鱼优化算法python源代码
下面是一个简单的鲸鱼优化算法的Python源代码示例:
```python
import numpy as np
def whale_optimization_algorithm(cost_function, num_iterations, num_variables, lb, ub, num_whales=10, a=2, b=1):
# 初始化鲸群位置
positions = np.random.uniform(lb, ub, (num_whales, num_variables))
# 初始化最佳解和最佳适应度
best_solution = None
best_fitness = float('inf')
for iteration in range(num_iterations):
for i in range(num_whales):
# 更新鲸鱼的位置
a = 2 - iteration * ((2) / num_iterations)
r1 = np.random.random()
r2 = np.random.random()
A = 2 * a * r1 - a
C = 2 * r2
distance_to_best = abs(C * best_solution - positions[i])
new_position = best_solution - A * distance_to_best
# 随机选择一只鲸鱼
random_whale_index = np.random.randint(0, num_whales)
random_whale = positions[random_whale_index]
# 更新鲸鱼的位置
distance_to_random = abs(2 * C * random_whale - positions[i])
new_position -= distance_to_random
# 限制位置在边界内
new_position = np.clip(new_position, lb, ub)
# 计算新位置的适应度
fitness = cost_function(new_position)
# 更新最佳解和最佳适应度
if fitness < best_fitness:
best_solution = new_position
best_fitness = fitness
# 更新鲸鱼的位置
positions[i] = new_position
return best_solution, best_fitness
```
这段代码实现了鲸鱼优化算法。你需要提供一个适应度函数 `cost_function`,以及算法的相关参数,如迭代次数 `num_iterations`、变量数量 `num_variables`、变量下界 `lb` 和上界 `ub`。该函数返回找到的最佳解和最佳适应度。请根据你的具体问题进行相应的修改和调整。
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