帮我找一下哈里斯鹰优化算法的代码
时间: 2024-03-22 19:18:58 浏览: 10
以下是一个简单的Python实现:
```
import random
class HarrisHawkOptimizer:
def __init__(self, objective_function, lb, ub, dimension, iteration, population_size, rpa):
self.objective_function = objective_function
self.lb = lb
self.ub = ub
self.dimension = dimension
self.iteration = iteration
self.population_size = population_size
self.rpa = rpa
def optimize(self):
population = [self.lb + (self.ub - self.lb) * random.random() for i in range(self.population_size * self.dimension)]
for i in range(self.iteration):
sorted_population = sorted(population, key=lambda x: self.objective_function(x))
top_hawk = sorted_population[0:self.dimension]
bottom_hawk = sorted_population[-self.dimension:]
for j in range(self.population_size):
current_hawk = population[j * self.dimension:(j + 1) * self.dimension]
if j < self.population_size / 2:
leader = top_hawk[random.randint(0, self.dimension - 1)]
current_hawk = [current_hawk[k] + random.random() * (leader - 2 * current_hawk[k]) for k in range(self.dimension)]
else:
leader = bottom_hawk[random.randint(0, self.dimension - 1)]
current_hawk = [current_hawk[k] + random.random() * (leader - 2 * current_hawk[k]) for k in range(self.dimension)]
for k in range(self.dimension):
if current_hawk[k] < self.lb:
current_hawk[k] = self.lb
if current_hawk[k] > self.ub:
current_hawk[k] = self.ub
population[j * self.dimension:(j + 1) * self.dimension] = current_hawk
sorted_population = sorted(population, key=lambda x: self.objective_function(x))
return sorted_population[0], self.objective_function(sorted_population[0])
```
其中,`objective_function`是需要优化的目标函数,`lb`和`ub`是变量的下界和上界,`dimension`是变量的个数,`iteration`是迭代次数,`population_size`是种群大小,`rpa`是随机扰动的幅度。调用方式如下:
```
hh = HarrisHawkOptimizer(objective_function, lb, ub, dimension, iteration, population_size, rpa)
best_solution, best_fitness = hh.optimize()
```