帮我写一个多目标优化算法的代码

当然可以，以下是一个多目标优化算法的代码示例：

import random

def multi_objective_optimization(objective_functions, num_generations, population_size):
    # Initialize population
    population = []
    for i in range(population_size):
        individual = [random.uniform(0, 1) for _ in range(len(objective_functions))]
        population.append(individual)

    # Evolution loop
    for generation in range(num_generations):
        # Evaluate fitness
        fitness_scores = []
        for individual in population:
            fitness_score = [f(individual) for f in objective_functions]
            fitness_scores.append(fitness_score)

        # Select parents
        parents = []
        for _ in range(population_size):
            parent1 = random.choice(population)
            parent2 = random.choice(population)
            parents.append((parent1, parent2))

        # Crossover and mutation
        offspring = []
        for parent1, parent2 in parents:
            child = []
            for i in range(len(objective_functions)):
                if random.random() < 0.5:
                    child.append(parent1[i])
                else:
                    child.append(parent2[i])
            for i in range(len(objective_functions)):
                if random.random() < 0.1:
                    child[i] += random.gauss(0, 0.1)
                    child[i] = max(0, min(1, child[i]))
            offspring.append(child)

        # Select survivors
        population = []
        for i in range(population_size):
            survivor = max(offspring, key=lambda x: fitness_scores[offspring.index(x)])
            population.append(survivor)
            offspring.remove(survivor)

    # Return Pareto front
    pareto_front = []
    for individual in population:
        is_pareto_dominant = True
        for other_individual in population:
            if individual != other_individual:
                is_pareto_dominant = all([individual[i] >= other_individual[i] for i in range(len(objective_functions))])
                if is_pareto_dominant:
                    break
        if is_pareto_dominant:
            pareto_front.append(individual)

    return pareto_front

这是一个简单的多目标优化算法，它使用了遗传算法的基本思想。它接受一个包含多个目标函数的列表，以及要运行的代数和种群大小。它返回一个 Pareto 前沿，其中包含所有非支配解。