多目标遗传算法code
时间: 2024-04-09 22:26:22 浏览: 113
多目标遗传算法(Multi-Objective Genetic Algorithm,MOGA)是一种用于解决多目标优化问题的进化算法。它基于遗传算法的思想,通过模拟自然界的进化过程,逐步搜索最优解的近似集合。
下面是一个简单的多目标遗传算法的代码示例:
```python
import random
# 定义目标函数
def objective_function(x):
return [x[0]**2, (x[0]-2)**2]
# 初始化种群
def initialize_population(population_size, num_variables):
population = []
for _ in range(population_size):
individual = [random.uniform(-5, 5) for _ in range(num_variables)]
population.append(individual)
return population
# 计算个体的适应度值
def calculate_fitness(individual):
return objective_function(individual)
# 选择操作
def selection(population, num_parents):
parents = []
for _ in range(num_parents):
parent = random.choice(population)
parents.append(parent)
return parents
# 交叉操作
def crossover(parents, offspring_size):
offspring = []
for _ in range(offspring_size):
parent1, parent2 = random.sample(parents, 2)
child = [parent1[i] if random.random() < 0.5 else parent2[i] for i in range(len(parent1))]
offspring.append(child)
return offspring
# 变异操作
def mutation(offspring):
mutated_offspring = []
for child in offspring:
mutated_child = [gene + random.uniform(-0.5, 0.5) for gene in child]
mutated_offspring.append(mutated_child)
return mutated_offspring
# 多目标遗传算法主函数
def multi_objective_genetic_algorithm(population_size, num_variables, num_generations):
population = initialize_population(population_size, num_variables)
for _ in range(num_generations):
parents = selection(population, population_size // 2)
offspring = crossover(parents, population_size - len(parents))
offspring = mutation(offspring)
population = parents + offspring
return population
# 测试
population_size = 100
num_variables = 1
num_generations = 50
result = multi_objective_genetic_algorithm(population_size, num_variables, num_generations)
print(result)
```
这段代码实现了一个简单的多目标遗传算法,其中定义了目标函数、初始化种群、计算适应度值、选择操作、交叉操作和变异操作等基本步骤。通过调用`multi_objective_genetic_algorithm`函数,可以得到最终的近似最优解集合。
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