蜣螂算法代码python
时间: 2023-11-16 18:07:41 浏览: 69
引用提供了蜣螂优化算法的Python代码,可以用于函数极值寻优。你可以在Python环境中运行这段代码,根据自己的需求进行修改和使用。以下是代码的基本结构和使用方法:
1.导入必要的库和模块:
import numpy as np
import random
2.定义蜣螂优化算法的类:
class DBO:
def __init__(self, func, dim, size, iter_num, lb, ub):
self.func = func # 待优化的目标函数
self.dim = dim # 变量的维度
self.size = size # 种群大小
self.iter_num = iter_num # 迭代次数
self.lb = lb # 变量的下界
self.ub = ub # 变量的上界
self.best_x = None # 最优解
self.best_f = np.inf # 最优解对应的函数值
self.swarm = np.random.uniform(low=self.lb, high=self.ub, size=(self.size, self.dim)) # 初始化种群
self.fitness = np.array([self.func(x) for x in self.swarm]) # 计算种群适应度
def optimize(self):
for i in range(self.iter_num):
for j in range(self.size):
# 选择一个随机的蜣螂
k = random.randint(0, self.size - 1)
while k == j:
k = random.randint(0, self.size - 1)
# 计算新位置
new_x = self.swarm[j] + np.random.uniform() * (self.swarm[j] - self.swarm[k])
# 边界处理
new_x = np.clip(new_x, self.lb, self.ub)
# 计算新适应度
new_f = self.func(new_x)
# 更新最优解
if new_f < self.best_f:
self.best_x = new_x
self.best_f = new_f
# 更新种群
if new_f < self.fitness[j]:
self.swarm[j] = new_x
self.fitness[j] = new_f
3.定义待优化的目标函数:
def func(x):
return np.sum(x ** 2)
4.创建DBO对象并运行优化算法:
dbo = DBO(func, dim=10, size=50, iter_num=1000, lb=-100, ub=100)
dbo.optimize()
5.输出最优解和最优解对应的函数值:
print('best_x:', dbo.best_x)
print('best_f:', dbo.best_f)