蛇优化算法的python迭代测试代码
时间: 2023-06-30 10:26:53 浏览: 41
以下是蛇优化算法的Python迭代测试代码示例:
```
import random
# 适应度函数
def fitness_function(x):
return sum([i**2 for i in x])
# 初始化种群
def initialize_population(population_size, num_variables):
population = []
for i in range(population_size):
individual = [random.uniform(-5.12, 5.12) for j in range(num_variables)]
population.append(individual)
return population
# 蛇优化算法
def snake_algorithm(population_size, num_variables, max_iter):
# 初始化种群
population = initialize_population(population_size, num_variables)
# 迭代
for i in range(max_iter):
# 计算适应度
fitness = [fitness_function(x) for x in population]
# 找到最优解
best_fitness = min(fitness)
best_individual = population[fitness.index(best_fitness)]
# 更新种群
new_population = []
for j in range(population_size):
# 计算权重
w = (j + 1) / population_size
# 计算方向
direction = [best_individual[k] - population[j][k] for k in range(num_variables)]
# 计算新个体
new_individual = [population[j][k] + w * direction[k] + random.uniform(-1, 1) for k in range(num_variables)]
# 添加到新种群
new_population.append(new_individual)
population = new_population
# 返回最优解
return best_individual
# 示例运行
best_solution = snake_algorithm(population_size=50, num_variables=10, max_iter=100)
print("最优解:", best_solution)
print("最优解的适应度:", fitness_function(best_solution))
```
在此示例中,我们定义了适应度函数`fitness_function`,它计算给定个体的适应度,我们使用了简单的二次函数作为示例。
然后,我们定义了初始化种群函数`initialize_population`,它创建一个具有随机值的给定大小的种群。
接下来,我们实现了蛇优化算法的主要部分。我们在每次迭代中计算种群中每个个体的适应度,然后找到最优解。然后,我们使用最优解来更新种群中的每个个体,并添加随机扰动来增加种群的多样性。
最后,我们运行示例并输出最优解和其适应度。请注意,由于算法的随机性质,每次运行结果可能会略有不同。