nsgaiii多目标算法代码 python

NSGA-III（Non-dominated Sorting Genetic Algorithm III）是一种常用的多目标遗传算法，用于解决多目标优化问题。它改进了早期版本NSGA-III的基本框架：

import numpy as np
from operator import attrgetter

class Individual:
    def __init__(self, objectives):
        self.objectives = objectives

def fitness_function(individuals):
    # 这里需要替换为你的实际目标函数计算
    return [individual.objectives for individual in individuals]

def dominance_sort(population):
    non_dominated_fronts = []
    objectives = population[0].objectives
    for ind in population:
        front = non_dominated_sort_helper(fronts=non_dominated_fronts,
                                          current_individual=ind,
                                          objectives=objectives)
        if front is not None:
            non_dominated_fronts.append(front)
    return non_dominated_fronts

def non_dominated_sort_helper(fronts, current_individual, objectives):
    dominated = False
    for front in fronts:
        for f1 in front:
            if dominates(f1.objectives, current_individual.objectives, objectives):
                dominated = True
                break
        else:
            # 如果当前个体未被其他个体支配，则加入非支配前沿
            fronts.append([current_individual])
            break
    else:
        # 如果所有前端都无法支配当前个体，则作为新前沿添加
        return None if len(fronts) == len(objectives) else [current_individual]
    
    return fronts[current_individual.index] if dominated else None

# ... 其他辅助函数如选择、交叉、变异等 ...

def nsga_iii(pop_size, generations, population):
    new_population = population
    for _ in range(generations):
        offspring = crossover_and_mutate(new_population)
        non_dominated_offspring = dominance_sort(offspring)
        crowding_distance(sorted(non_dominated_offspring))  # 计算拥挤度
        new_population = elitist_selection(new_population + non_dominated_offspring)

    return new_population

def crowding_distance(population):
    pass  # 实现计算每个个体的拥挤度

def elitist_selection(population):
    pass  # 实现精英保留策略的选择

# 调用nsga_iii并获取最终解集
final_pop = nsga_iii(pop_size, num_generations, initial_population)

注意，这个例子提供了一个基础框架，并没有包括具体的拥挤度计算和选择策略，这些部分需要你自己实现。此外，这只是一个简化的版本，实际应用中还需要处理复杂边界条件和其他细节。