在上面NSGA-II算法求解的代码中，若数据是直接写在代码中的，完整的代码该是什么

好的，以下是一个使用 NSGA-II 算法求解多目标优化问题的 Python 代码示例，其中数据是直接写在代码中的：

import random
import math

# 定义问题
# 目标1: 最小化函数f1(x)
# 目标2: 最小化函数f2(x)
def problem(x):
    f1 = x[0]**2 + x[1]**2
    f2 = (x[0]-1)**2 + x[1]**2
    return (f1, f2)

# 定义NSGA-II算法
def nsga2(popsize, generations, bounds):
    # 初始化种群
    pop = []
    for i in range(popsize):
        indv = []
        for j in range(len(bounds)):
            indv.append(random.uniform(bounds[j][0],bounds[j][1]))
        pop.append(indv)
    
    # 循环迭代
    for gen in range(generations):
        # 计算适应度
        fitvals = []
        for indv in pop:
            fitvals.append(problem(indv))
        
        # 计算拥挤程度
        crowding = []
        for i in range(len(pop)):
            crowding.append(0)
        for i in range(len(bounds)):
            newpop = sorted(pop, key=lambda x:x[i])
            crowding[0] = math.inf
            crowding[-1] = math.inf
            for j in range(1, len(pop)-1):
                crowding[j] += (newpop[j+1][i] - newpop[j-1][i])/(bounds[i][1]-bounds[i][0])
        
        # 选择新一代
        newpop = []
        for i in range(popsize):
            p1 = random.randint(0, popsize-1)
            p2 = random.randint(0, popsize-1)
            if crowding[p1] > crowding[p2]:
                newpop.append(pop[p1])
            else:
                newpop.append(pop[p2])
        
        # 变异
        for i in range(popsize):
            if random.uniform(0,1) < 0.1:
                indv = []
                for j in range(len(bounds)):
                    indv.append(random.uniform(bounds[j][0],bounds[j][1]))
                newpop[i] = indv
        
        # 更新种群
        pop = newpop
    
    # 返回非支配解
    front = []
    for indv in pop:
        dominated = False
        for other in pop:
            if indv != other and problem(indv) < problem(other):
                dominated = True
                break
        if not dominated:
            front.append(indv)
    
    return front

# 测试
bounds = [(-5,5),(-5,5)]
front = nsga2(100, 100, bounds)
for indv in front:
    print(indv, problem(indv))

这是一个比较简单的示例，仅用两个目标函数来演示 NSGA-II 算法的求解过程。您可以根据自己的需求修改问题定义和算法参数，来解决多目标优化问题。