ga结合sa混合算法代码
时间: 2023-07-28 12:04:37 浏览: 55
GA(遗传算法)和SA(模拟退火算法)是两种常用的优化算法。下面是将GA和SA结合的一种混合算法的示例代码:
```python
import random
import math
# 初始化种群
def initial_population(population_size):
population = []
for i in range(population_size):
solution = []
for j in range(10): # 假设优化问题中有10个参数
solution.append(random.randint(0, 1)) # 随机生成二进制串
population.append(solution)
return population
# 计算适应度
def fitness(solution):
x = binary_to_real(solution) # 将二进制串转换为实数
return math.sin(x) # 简单示例,使用sin函数作为适应度函数
# 二进制转换为实数
def binary_to_real(solution):
x = 0
for i in range(len(solution)):
x += solution[i] * math.pow(2, i)
return x
# 选择操作
def selection(population):
sorted_population = sorted(population, key=fitness, reverse=True) # 根据适应度对种群进行排序
selected_population = sorted_population[:len(population) // 2] # 选择适应度较高的一半个体
return selected_population
# 交叉操作
def crossover(selected_population):
offspring_population = []
for i in range(len(selected_population)):
parent1 = random.choice(selected_population)
parent2 = random.choice(selected_population)
crossover_point = random.randint(1, len(parent1) - 1) # 随机选择交叉点
offspring = parent1[:crossover_point] + parent2[crossover_point:] # 交叉生成子代
offspring_population.append(offspring)
return offspring_population
# 变异操作
def mutation(offspring_population, mutation_rate):
mutated_population = []
for i in range(len(offspring_population)):
offspring = offspring_population[i]
for j in range(len(offspring)):
if random.random() < mutation_rate: # 以一定概率进行变异
offspring[j] = 1 - offspring[j] # 变异操作,将0变为1,将1变为0
mutated_population.append(offspring)
return mutated_population
# 模拟退火操作
def simulated_annealing(population):
temperature = 100 # 初始温度
cooling_rate = 0.01 # 降温速率
while temperature > 1:
solution = random.choice(population)
current_fitness = fitness(solution)
new_solution = mutation([solution], 1.0)[0] # 以1.0的概率进行变异得到新解
new_fitness = fitness(new_solution)
if new_fitness >= current_fitness: # 如果新解更优秀,则接受新解
solution = new_solution
else:
acceptance_probability = math.exp((new_fitness - current_fitness) / temperature) # 计算接受概率
if random.random() < acceptance_probability:
solution = new_solution
temperature *= 1 - cooling_rate # 降温
return solution
# 主函数
def main():
population_size = 100 # 种群大小
mutation_rate = 0.01 # 变异率
iterations = 100 # 迭代次数
population = initial_population(population_size)
for i in range(iterations):
selected_population = selection(population)
offspring_population = crossover(selected_population)
mutated_population = mutation(offspring_population, mutation_rate)
population = selected_population + mutated_population
best_solution = simulated_annealing(population)
print("Iteration:", i + 1, "Best Solution:", best_solution, "Fitness:", fitness(best_solution))
if __name__ == '__main__':
main()
```
上述代码实现了一个简单的优化问题求解过程,其中GA部分负责生成初始种群、选择与交叉操作,而SA部分通过模拟退火操作对种群进行调整。通过迭代进行多次进化和退火,最终得到一个较优的解。该代码仅供参考,实际应用中需要根据具体问题进行相应的修改和调整。