moga算法的python代码示例

MOGA（多目标遗传算法）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MOGA算法的Python代码示例：

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):
    objectives = objective_function(individual)
    return objectives

# 非支配排序
def non_dominated_sort(population):
    fronts = []
    ranks = [0] * len(population)
    domination_count = [0] * len(population)
    dominated_solutions = [[] for _ in range(len(population))]
    for i in range(len(population)):
        for j in range(i+1, len(population)):
            if dominates(population[i], population[j]):
                domination_count[j] += 1
                dominated_solutions[i].append(j)
            elif dominates(population[j], population[i]):
                domination_count[i] += 1
                dominated_solutions[j].append(i)

        if domination_count[i] == 0:
            ranks[i] = 0
            if i not in fronts:
                fronts.append(i)

    while fronts:
        next_fronts = []
        for i in fronts:
            for j in dominated_solutions[i]:
                domination_count[j] -= 1
                if domination_count[j] == 0:
                    ranks[j] = ranks[i] + 1
                    if j not in next_fronts:
                        next_fronts.append(j)
        fronts = next_fronts

    return ranks

# 判断个体是否支配另一个个体
def dominates(individual1, individual2):
    objectives1 = calculate_fitness(individual1)
    objectives2 = calculate_fitness(individual2)
    return all(obj1 <= obj2 for obj1, obj2 in zip(objectives1, objectives2)) and any(obj1 < obj2 for obj1, obj2 in zip(objectives1, objectives2))

# 选择操作
def selection(population, ranks, num_parents):
    parents = []
    while len(parents) < num_parents:
        front = random.choice(ranks)
        front_individuals = [i for i in range(len(population)) if ranks[i] == front]
        best_individual = random.choice(front_individuals)
        parents.append(population[best_individual])
    return parents

# 交叉操作
def crossover(parents, offspring_size):
    offspring = []
    while len(offspring) < offspring_size:
        parent1, parent2 = random.sample(parents, 2)
        child = [random.uniform(min(gene1, gene2), max(gene1, gene2)) for gene1, gene2 in zip(parent1, parent2)]
        offspring.append(child)
    return offspring

# 变异操作
def mutation(offspring):
    mutated_offspring = []
    for individual in offspring:
        mutated_individual = [gene + random.uniform(-0.5, 0.5) for gene in individual]
        mutated_offspring.append(mutated_individual)
    return mutated_offspring

# MOGA算法主函数
def moga_algorithm(population_size, num_variables, num_generations):
    population = initialize_population(population_size, num_variables)
    for _ in range(num_generations):
        ranks = non_dominated_sort(population)
        parents = selection(population, ranks, population_size//2)
        offspring = crossover(parents, population_size - len(parents))
        mutated_offspring = mutation(offspring)
        population = parents + mutated_offspring
    return population

# 测试MOGA算法
population_size = 100
num_variables = 1
num_generations = 50

final_population = moga_algorithm(population_size, num_variables, num_generations)
for individual in final_population:
    print(f"Individual: {individual}, Fitness: {calculate_fitness(individual)}")

这是一个简单的MOGA算法的实现，其中包括目标函数的定义、种群初始化、适应度计算、非支配排序、选择、交叉和变异等操作。你可以根据自己的需求进行修改和扩展。希望对你有帮助！