MOEAD算法改进python

MOEA/D (Multi-Objective Evolutionary Algorithm Based on Decomposition) 是一种用于多目标优化问题的进化算法。它通过将多目标问题转化为一组单目标子问题，并使用分解方法来解决这些子问题。下面是一个简单的示例代码，展示了如何实现MOEA/D算法的改进版本：

import numpy as np

def initialization(pop_size, num_objectives, num_variables):
    population = np.random.rand(pop_size, num_variables)
    weights = np.random.rand(pop_size, num_objectives)
    return population, weights

def decomposition(population, weights):
    objectives = np.zeros((population.shape[0], weights.shape[1]))
    for i in range(population.shape[0]):
        for j in range(weights.shape[1]):
            objectives[i, j] = np.sum(population[i] * weights[i, j])
    return objectives

def selection(objectives, num_neighbors):
    neighbors = np.zeros((objectives.shape[0], num_neighbors), dtype=int)
    for i in range(objectives.shape[0]):
        distances = np.linalg.norm(objectives - objectives[i], axis=1)
        indices = np.argsort(distances)[:num_neighbors]
        neighbors[i] = indices
    return neighbors

def crossover(parents, num_offspring):
    offspring = []
    for _ in range(num_offspring):
        parent1, parent2 = np.random.choice(parents, 2, replace=False)
        alpha = np.random.rand()
        child = alpha * parent1 + (1 - alpha) * parent2
        offspring.append(child)
    return np.array(offspring)

def mutation(individuals, mutation_rate):
    for i in range(individuals.shape[0]):
        for j in range(individuals.shape[1]):
            if np.random.rand() < mutation_rate:
                individuals[i, j] = np.random.rand()
    return individuals

def moead(pop_size, num_generations, num_objectives, num_variables, num_neighbors, mutation_rate):
    population, weights = initialization(pop_size, num_objectives, num_variables)
    objectives = decomposition(population, weights)
    
    for _ in range(num_generations):
        neighbors = selection(objectives, num_neighbors)
        offspring = crossover(population[neighbors], pop_size)
        offspring = mutation(offspring, mutation_rate)
        offspring_objectives = decomposition(offspring, weights)
        
        for i in range(pop_size):
            for j in neighbors[i]:
                if np.all(offspring_objectives[i] <= objectives[j]):
                    population[j] = offspring[i]
                    objectives[j] = offspring_objectives[i]
        
    return population, objectives

# 示例用法
pop_size = 100  # 种群大小
num_generations = 100  # 迭代次数
num_objectives = 2  # 目标数量
num_variables = 10  # 变量数量
num_neighbors = 5  # 邻居数量
mutation_rate = 0.1  # 变异率

population, objectives = moead(pop_size, num_generations, num_objectives, num_variables, num_neighbors, mutation_rate)
print("Final Population:")
print(population)
print("Final Objectives:")
print(objectives)

这是一个简单的MOEA/D算法改进版本的实现，你可以根据自己的问题进行修改和扩展。请注意，这只是一个基本示例，并不包含所有可能的改进措施。你可以根据具体情况进行进一步的改进和优化。希望对你有帮助！