多目标规划遗传算法代码

多目标规划遗传算法是一种用于解决多目标优化问题的进化算法。下面是一个简单的多目标规划遗传算法的代码示例：

import random

# 定义目标函数
def objective_function(x):
    return [x[0]**2, (x[0]-2)**2]

# 定义个体类
class Individual:
    def __init__(self, chromosome_length, lower_bound, upper_bound):
        self.chromosome = [random.uniform(lower_bound, upper_bound) for _ in range(chromosome_length)]
        self.fitness = None

    def evaluate(self):
        self.fitness = objective_function(self.chromosome)

# 定义种群类
class Population:
    def __init__(self, population_size, chromosome_length, lower_bound, upper_bound):
        self.population = [Individual(chromosome_length, lower_bound, upper_bound) for _ in range(population_size)]

    def evaluate(self):
        for individual in self.population:
            individual.evaluate()

    def select_parents(self, num_parents):
        parents = []
        sorted_population = sorted(self.population, key=lambda x: x.fitness)
        for i in range(num_parents):
            parents.append(sorted_population[i])
        return parents

    def crossover(self, parents, offspring_size):
        offspring = []
        for _ in range(offspring_size):
            parent1 = random.choice(parents)
            parent2 = random.choice(parents)
            child = Individual(len(parent1.chromosome), min(parent1.chromosome), max(parent1.chromosome))
            for i in range(len(parent1.chromosome)):
                if random.random() < 0.5:
                    child.chromosome[i] = parent1.chromosome[i]
                else:
                    child.chromosome[i] = parent2.chromosome[i]
            offspring.append(child)
        return offspring

    def mutate(self, offspring, mutation_rate):
        for individual in offspring:
            for i in range(len(individual.chromosome)):
                if random.random() < mutation_rate:
                    individual.chromosome[i] = random.uniform(min(individual.chromosome), max(individual.chromosome))

# 定义多目标规划遗传算法类
class MOGA:
    def __init__(self, population_size, chromosome_length, lower_bound, upper_bound, num_generations, num_parents, offspring_size, mutation_rate):
        self.population = Population(population_size, chromosome_length, lower_bound, upper_bound)
        self.num_generations = num_generations
        self.num_parents = num_parents
        self.offspring_size = offspring_size
        self.mutation_rate = mutation_rate

    def run(self):
        for _ in range(self.num_generations):
            self.population.evaluate()
            parents = self.population.select_parents(self.num_parents)
            offspring = self.population.crossover(parents, self.offspring_size)
            self.population.mutate(offspring, self.mutation_rate)
            self.population.population = parents + offspring

        self.population.evaluate()
        best_individual = min(self.population.population, key=lambda x: x.fitness)
        return best_individual.chromosome

# 使用示例
population_size = 50
chromosome_length = 1
lower_bound = -5
upper_bound = 5
num_generations = 100
num_parents = 20
offspring_size = 30
mutation_rate = 0.1

moga = MOGA(population_size, chromosome_length, lower_bound, upper_bound, num_generations, num_parents, offspring_size, mutation_rate)
best_solution = moga.run()
print("Best solution:", best_solution)

这段代码实现了一个简单的多目标规划遗传算法，其中包括个体类、种群类和多目标规划遗传算法类。通过调整参数，可以适应不同的问题和需求。