双种群协同进化的代码
时间: 2024-05-02 18:15:14 浏览: 17
双种群协同进化是一种优化算法,它通过同时演化两个种群来解决复杂的优化问题。下面是一个简单的双种群协同进化的代码示例:
```python
import numpy as np
# 定义目标函数
def objective_function(x):
return x**2
# 初始化种群
def initialize_population(population_size, lower_bound, upper_bound):
population = np.random.uniform(lower_bound, upper_bound, population_size)
return population
# 计算适应度
def calculate_fitness(population):
fitness = objective_function(population)
return fitness
# 选择操作
def selection(population, fitness, selection_size):
selected_indices = np.argsort(fitness)[:selection_size]
selected_population = population[selected_indices]
return selected_population
# 交叉操作
def crossover(parents, crossover_rate):
offspring = []
for i in range(len(parents) // 2):
if np.random.rand() < crossover_rate:
parent1 = parents[i * 2]
parent2 = parents[i * 2 + 1]
child1 = (parent1 + parent2) / 2
child2 = (parent1 - parent2) / 2
offspring.extend([child1, child2])
else:
offspring.extend([parents[i * 2], parents[i * 2 + 1]])
return np.array(offspring)
# 变异操作
def mutation(population, mutation_rate, lower_bound, upper_bound):
mutated_population = population.copy()
for i in range(len(mutated_population)):
if np.random.rand() < mutation_rate:
mutated_population[i] += np.random.uniform(lower_bound, upper_bound)
return mutated_population
# 双种群协同进化算法
def dual_population_cooperative_evolution(population_size, lower_bound, upper_bound, num_generations, selection_size, crossover_rate, mutation_rate):
population1 = initialize_population(population_size, lower_bound, upper_bound)
population2 = initialize_population(population_size, lower_bound, upper_bound)
for generation in range(num_generations):
fitness1 = calculate_fitness(population1)
fitness2 = calculate_fitness(population2)
selected_population1 = selection(population1, fitness1, selection_size)
selected_population2 = selection(population2, fitness2, selection_size)
offspring1 = crossover(selected_population1, crossover_rate)
offspring2 = crossover(selected_population2, crossover_rate)
mutated_population1 = mutation(offspring1, mutation_rate, lower_bound, upper_bound)
mutated_population2 = mutation(offspring2, mutation_rate, lower_bound, upper_bound)
population1 = np.concatenate((population1, mutated_population2))
population2 = np.concatenate((population2, mutated_population1))
best_solution1 = population1[np.argmin(calculate_fitness(population1))]
best_solution2 = population2[np.argmin(calculate_fitness(population2))]
return best_solution1, best_solution2
# 参数设置
population_size = 100
lower_bound = -10
upper_bound = 10
num_generations = 100
selection_size = 50
crossover_rate = 0.8
mutation_rate = 0.1
# 运行双种群协同进化算法
best_solution1, best_solution2 = dual_population_cooperative_evolution(population_size, lower_bound, upper_bound, num_generations, selection_size, crossover_rate, mutation_rate)
print("最优解1:", best_solution1)
print("最优解2:", best_solution2)
```
这段代码实现了一个简单的双种群协同进化算法,其中包括目标函数的定义、种群的初始化、适应度计算、选择操作、交叉操作和变异操作等。你可以根据自己的需求进行修改和扩展。