遗传算法求功率潮流代码
时间: 2023-07-23 18:57:10 浏览: 43
以下是一个简化的遗传算法求解功率潮流问题的示例代码:
```python
import numpy as np
# 1. 初始化种群
def initialize_population(pop_size, num_variables):
population = []
for _ in range(pop_size):
individual = np.random.uniform(low=0.9, high=1.1, size=num_variables) # 随机生成初始解
population.append(individual)
return population
# 2. 适应度评估
def evaluate_fitness(individual):
# 根据个体的变量值计算适应度
# 这里可以根据具体的功率潮流问题定义适应度函数,例如最小化功率损耗或满足电压和功率约束
fitness = ...
return fitness
# 3. 选择操作
def selection(population, fitness_values, num_parents):
parents = []
fitness_sum = np.sum(fitness_values)
probabilities = fitness_values / fitness_sum
for _ in range(num_parents):
selected_index = np.random.choice(len(population), p=probabilities)
parents.append(population[selected_index])
return parents
# 4. 交叉操作
def crossover(parents, num_offsprings):
offsprings = []
for _ in range(num_offsprings):
parent1, parent2 = np.random.choice(parents, size=2, replace=False)
offspring = np.average([parent1, parent2], axis=0) # 交叉操作,这里使用简单的平均值
offsprings.append(offspring)
return offsprings
# 5. 变异操作
def mutation(offsprings, mutation_rate):
for offspring in offsprings:
if np.random.uniform() < mutation_rate:
# 随机选择一个变量进行变异操作
index = np.random.randint(len(offspring))
offspring[index] = np.random.uniform(low=0.9, high=1.1)
return offsprings
# 6. 遗传算法求解功率潮流问题
def genetic_algorithm(population_size, num_variables, num_parents, num_offsprings, mutation_rate, num_generations):
# 初始化种群
population = initialize_population(population_size, num_variables)
for generation in range(num_generations):
# 适应度评估
fitness_values = [evaluate_fitness(individual) for individual in population]
# 选择操作
parents = selection(population, fitness_values, num_parents)
# 交叉操作
offsprings = crossover(parents, num_offsprings)
# 变异操作
offsprings = mutation(offsprings, mutation_rate)
# 更新种群
population = parents + offsprings
# 返回最优解
best_individual = max(population, key=evaluate_fitness)
return best_individual
# 使用示例
population_size = 50
num_variables = 10
num_parents = 10
num_offsprings = 20
mutation_rate = 0.1
num_generations = 100
best_solution = genetic_algorithm(population_size, num_variables, num_parents, num_offsprings, mutation_rate, num_generations)
print("Best solution:", best_solution)
```
请注意,这只是一个简化的示例代码,具体的实现可能需要根据具体的功率潮流问题进行调整和扩展。同时,适应度函数的设计也需要根据问题的具体要求进行定义。
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