遗传算法得到最优目标值及最优个体，一次迭代即产生子代1

import math
import random
from random import sample
import numpy as np
from numpy import *
import geatpy as ea
import xlrd

##已知数据
q1='F:\共享车选址调度\共享汽车数据\候选点之间的OD(13).xlsx'
T1='F:\共享车选址调度\共享汽车数据\候选点之间的最短期望时间Tik（13）.xlsx'
wt1='F:\共享车选址调度\共享汽车数据\需求中心到备选点的步行时间（50行13列）.xlsx'
D1='F:\共享车选址调度\共享汽车数据\每个需求中心的需求量（50网格）.xlsx'
# ####excel转为矩阵
def excel_to_matrix(path,a):  #路径，sheet
    table = xlrd.open_workbook(path).sheets()[a]#获取第一个sheet表
    row = table.nrows  # 行数
    col = table.ncols  # 列数
    datamatrix = np.zeros((row, col))#生成一个nrows行ncols列，且元素均为0的初始矩阵
    for x in range(col):
        cols = np.matrix(table.col_values(x))  # 把list转换为矩阵进行矩阵操作
        datamatrix[:, x] = cols # 按列把数据存进矩阵中
    return datamatrix
q=excel_to_matrix(q1,0)
T=excel_to_matrix(T1,0)
wt=excel_to_matrix(wt1,0)
D=excel_to_matrix(D1,1)  # 多维数组，50行1列
# #############参数##########################
Cs=1*10
Cp=12*10
Cv=56*10
Ce=6
M=10000
alpha=0.8
beta=0.5
gama=2
I=13
J=50
Pmax=100
w1=50
H =500000  # 成本的最大值
##########################################
##距离衰减
s=zeros((J, I))  # 创建一个J*I的零矩阵，矩阵这里zeros函数的参数是一个tuple类型(J,I)
for i in range(I):
    for j in range(J):
        if wt[j,i]<w1:
            a=-(wt[j,i])**4+w1**4
            b=(w1**4)*(math.exp((wt[j,i]/40)**3))
            s[j,i]=a/b
        elif wt[j,i]>w1:
            s[j,i]=0
## qik/qi
n=zeros((J, I))
for i in range(I):
    for k in range(I):
        if sum(q[i])>0:  #每一行的和
            n[i,k]=q[i,k]/sum(q[i])
        else:
            n[i,k]=0
'''1.目标函数'''
def objection(X,P,V,z):
    z0=np.array(z).reshape(J,I)  #列表转一维数组，转多维数组
    for i in range(I):
        Ti = T[i, :]  # 多维数组，第i行
        ni = n[i, :]
        ui= [sum(D[:,0]*z0[:,i]*s[:,i]) for i in range(I)] # 输出为一个列表，其中含有13个元素，表示分布从13个备选站点出发的租车辆
        # ob = [Cs * X[i] + Cp * P[i] + Cv * V[i] + Ce * sum(Ti * ni) * ui[i] for i in range(I)]  # 输出为一个列表，其中含有13个元素，每个元素对应一个站点的成本
        obj=sum([Cs * X[i] + Cp * P[i] + Cv * V[i] + Ce * sum(Ti * ni) * ui[i] for i in range(I)])  #输出结果为总成本，约29万
    return obj


'''2.实数编码'''
def code():
    Chrom = []
    X_0 = np.random.randint(0, 2, I)  # 随机产生13个只含0,1元素的列表,13为初始种群的个数
    Chrom.extend(X_0)
    V_0 = np.random.randint(0, 101, I)  # 随机产生13个0到100中的正整数的列表
    Chrom.extend(V_0)
    P_0 = np.random.randint(0, 81, I)  # 随机产生13个0到80中的正整数的列表.80=100*alpha
    Chrom.extend(P_0)
    z0 = np.random.random((J, I))  # J=50,I=30，50行13列的二维数组
    z_0 = z0.flatten()  # 转化为一维数组,按行转化
    Chrom.extend(z_0)
    u_0 = [sum(D[j, 0] * z_0[I * j + i] * s[j, i] * n[i, k] for j in range(J)) for i in range(I) for k in range(I)]  # 含有I*I=169个元素，uik
    Chrom.extend(u_0)
    # print(Chrom)  # 输出为一个列表，其中共（50+3）*13+169=858个元素
    return Chrom

def initial_pop(Nind):
    pop=[]
    i=0
    while i<=Nind:
        IND=code()
        pop.append(IND)
        i += 1
    return pop
'''3.区分各个变量'''
def Chrom_to_var(Chrom):
    X=Chrom[:I]  #前13个元素为X
    V=Chrom[I:2*I]
    P=Chrom[2*I:3*I]
    z=Chrom[3*I:3*I+I*J]
    u=Chrom[3*I+I*J:]
    return X,V,P,z,u
'''5.需求约束'''
def demand_feasible(Chrom):
    X,V,P,z,u=Chrom_to_var(Chrom)
    g5 =[z[I*j+i]-X[i] for i in range(I) for j in range(J)]  # J*I个约束
    # g5_n = np.array(g5).reshape(J, I)  # 将一维列表转化为J行I列的数组
    # g5_c = [sum(g5_n[:, i]) for i in range(I)]  # 对每一列求和，得到每个染色体（每个站点）对应的约束，输出为含有I个元素的列表
    g6 =[sum(z[I * j + i] for i in range(I))-1 for j in range(J)]  # J个约束
    g7 =[sum(D[j,0]*z[I*j+i] for i in range(I))-D[j,0] for j in range(J)]  # J个约束
    g8 =[sum(D[j,0]*z[I*j+i] for j in range(J))-P[i] for i in range(I)]  # I个约束
    g9 =[beta*sum(D[j,0] for j in range(J))-sum(sum(D[j,0]*z[I*j+i]*s[j,i] for i in range(I)) for j in range(J))]  # 1个约束
    # g10 =[sum(D[j,0]*z[I*j+i]*s[j,i]*n[i,k] for j in range(J))-u[I*i+k] for i in range(I) for k in range(I)]  # I*I个约束
    # g11 =[u[I*i+k]-sum(D[j,0]*z[I*j+i]*s[j,i]*n[i,k] for j in range(J)) for i in range(I) for k in range(I)]  # I*I个约束
    g12 =[sum(u[I*i+k] for k in range(I))-V[i] for i in range(I)]  # I个约束
    g13 =[sum(u[I*i+k] for i in range(I) for k in range(I))*0.657+11.89-sum(V[i] for i in range(I))]  # 1个约束
    g_1 = g5+g6+g7+g8+g9+g12+g13  # 将前面所以约束放到同一个列表中,1116个元素
    g_pos = sum([abs(i) for i in g_1])  # 将g_1中的元素转化为正值并求和
    num_cons = I*J+J+J+I+1+I+1
    rhs = [0] * num_cons  # 输出为有I*J+J+J+I+1+I*I+I*I+I+1个元素的列表，每个元素都是0
    cons_1 = [g_1[m] <= rhs[m] for m in range(len(rhs))]  # 前面将约束写为<=0的形式，这里进行判断，输出为[True,False……]
    return g_1,g_pos,cons_1

'''6.产生初始种群'''
def initial_pop(Nind):  # Nind为种群数量
    pop=[]
    i=0
    while i<Nind:
        IND=code()
        pop.append(IND)
        i += 1
    return pop  # 输出为含有Nind个染色体的种群，列表

'''7.计算适应度'''
def get_fitness(pop, Nind,C,c):  # C是一个足够大的数，目标函数的最大估计
    fit=[]
    for i in range(Nind):
        X=Chrom_to_var(pop[i])[0]
        P = Chrom_to_var(pop[i])[1]
        V = Chrom_to_var(pop[i])[2]
        z = Chrom_to_var(pop[i])[3]
        H=objection(X,P,V,z)
        if all(demand_feasible(pop[i])[2]) is True:
            fitness=C-H # Z为目标函数
        else:
            fitness=C-H-c*demand_feasible(pop[i])[1] #g(x)为约束条件，c为正值常数
        fit.append(fitness)  # 将每次循环的结果存入列表中，输出为[89688.98555388054, 144668.3419467736, 97700.61376721218, 173935.77171023752, 160989.79620219418]
    return fit

'''8.适应度排序'''
def fit_sort(fit):
    fit2 = fit[:]   # 通过分片操作将列表FitnV_l的元素全部拷贝给FitnV2，产生一个新副本,不改变fit的适应度排序，便于后面染色体的索引
    fit2.sort(reverse=True)  # 适应度降序排序，产生一个排序好的副本，同时保持原有列表不变
    return fit2
'''8.得到精英个体'''
def get_elite(pop,fit,fit2,eli_n):
    Se_eli =fit2[:eli_n]  #选择前eli_n个个体直接进入子代
    See_ind = [fit.index(i) for i in Se_eli]  #得到被选择个体的索引
    pop_e=[]
    for i in See_ind:
        pop_e.append(pop[i])
    return pop_e  # 返回值为直接进入子代的精英个体

'''9.自然选择：轮盘赌'''
def selection(pop,fit,Nsel):
    # USe_eli = fit2[eli_n:]  # 精英选择中未被选择的个体
    USe_eli1=np.array(fit).reshape(7,1)  # 转化为多维数组，N为种群数量
    se_ind =ea.rws(USe_eli1,Nsel) # 得到被选择个体的索引， Nsel被选择个体的数目
    pop_s = []
    for i in se_ind:
        pop_s.append(pop[i])
    return pop_s

# pop_1 = selection(pop,fit,Nsel)  # 参与交叉的个体
# print(pop_1)

'''10.交叉：线性交叉'''
def crossover(pop_s,pc):
    j = 0
    while j <= 2:
        chrom = sample(pop_s, 2)  # 抽取pop_1中的两个个体染色体进行交叉。列表
        # print(chrom)
        chrom_ind = [pop_s.index(i) for i in chrom]  # 得到进行交叉的染色体在pop_1中的索引
        # n_chorm=pop_1[row_rand_array[2:]]
        c_point1 = random.randint(0, len(chrom[0])-1)
        c_point2 = random.randint(0, len(chrom[0])-1)
        if c_point1 <= c_point2:
            s_point = c_point1
            e_point = c_point2
        else:
            s_point = c_point2
            e_point = c_point1
        # print(s_point, e_point)
        r = random.random()
        if r <= pc:
            new_C = chrom
            # print(new_C)
        else:
            a = random.random()
            b = random.random()
            # print(a, b)
            chrom_n1 = chrom[0]  # 父代1
            chrom_n2 = chrom[1]  # 父代2
            for i in range(s_point, e_point + 1):  # 遍历每个交叉点
                Ge1 = chrom_n1[i]
                Ge2 = chrom_n2[i]
                # print(Ge1, Ge2)
                if 0<=i<=3*I:
                    Ge1 = int(Ge2 + a * (Ge1 - Ge2))
                    Ge2 = int(Ge1 + b * (Ge2 - Ge1)) # 交叉后的基因
                else:
                    Ge1 = Ge2 + a * (Ge1 - Ge2)
                    Ge2 = Ge1 + b * (Ge2 - Ge1)
                # print(Ge1, Ge2)
                chrom_n1[i] = Ge1
                chrom_n2[i] = Ge2 #更新染色体
                # print(chorm_n1, chorm_n2)
            # print(chorm_n1, chorm_n2)  # 注意缩进，只要最后每个点交叉后最终的迭代结果
            pop_s[chrom_ind[0]] = chrom_n1
            pop_s[chrom_ind[1]] = chrom_n2  # 更新整个种群，将交叉后的染色体存入种群中
        j+=2
    return pop_s

def mutate(pop_s,pm):
    j = 0
    while j <= 1:
        px = len(pop_s)  # 种群中染色体的数目
        py = len(pop_s[0])  #每个染色体中基因的数目
        im = random.randint(0, px - 1)  # im为进行变异的染色体的索引
        # chrom_m = pop_1[im]  # 进行变异的染色体
        # 判断每条染色体的每个基因是否需要变异
        for i in range(py):
            if (0.5 <= pm):
                pop_s[im][i] = pop_s[im][i]
            else:
                if 0 <= i <=  I-1:
                    if pop_s[im][i]==0:
                        pop_s[im][i] = 1  # 变量的上下界
                    else:
                        pop_s[im][i]=0
                elif i <=2*I-1:
                    pop_s[im][i] = random.randint(0, 100)
                elif i <=3*I-1:
                    pop_s[im][i] = random.randint(0, 80)
                else:
                    pop_s[im][i] = random.uniform(0, 1)
        j+=1
    return pop_s
pop = initial_pop(7)  # 初始种群，内有7个个体，列表
print(pop)
print(len(pop))
fit = get_fitness(pop, 7, 500000, 0.5)  # 得到每个个体的适应度，列表
fit2 = fit_sort(fit)  #得到降序排列的适应度
pop_e = get_elite(pop, fit, fit2, 2)  # 选取2个精英个体直接进入子代
pop_s = selection(pop, fit, 6)  # 从初始种群中选择6个进行遗传操作
pop_c = crossover(pop_s, 0.7)  # 得到交叉后的个体，列表，内含6个个体
# print(pop_c)
# print(len(pop_c))
fit_c=get_fitness(pop_c,6,500000,0.5)  # 得到交叉后的种群中的每个个体的适应度
fit_c2=fit_sort(fit_c)  #得到降序排列的适应度
pop_e2=get_elite(pop_c,fit_c,fit_c2,2)  # 从交叉后的种群中选择eli_n个直接进入子代
pop_m = mutate(pop_c, 0.2)  # 得到变异后的种群
# print(pop_m)
# print(len(pop_m))
fit_m=get_fitness(pop_m,6,500000,0.5)  # 得到交叉后的种群中的每个个体的适应度
fit_m2=fit_sort(fit_m)
pop_e3 = get_elite(pop_c, fit_m, fit_m2, 3) # 种群中共7个个体，父代和交叉后各两个直接进入子代，则变异后只需适应度前3的个体
pop_new=pop_e+pop_e2+pop_e3
print(pop_new)
print(len(pop_new))
obj=[]
for i in range(len(pop_new)):
    Chrom_a = pop_new[i]  # 子代中的每一个个体
    X = Chrom_to_var(Chrom_a)[0]
    P = Chrom_to_var(Chrom_a)[1]
    V = Chrom_to_var(Chrom_a)[2]
    z = Chrom_to_var(Chrom_a)[3]
    obj_a = objection(X, P, V, z)  # 一个个体的目标函数值
    obj.append(obj_a)  # obj中包括每一个个体的目标值，列表
    obj_max = max(obj)  # 得到子代中的最优个体的目标值
    opti_index = obj.index(obj_max)  # 得到最优目标值的索引
    chrom_opti = pop_new[opti_index]  # 得到最优目标值对应的最优个体
print(obj)
print(obj_max)
print(opti_index)
print(chrom_opti)
'''输出
新子代，有7个个体，每个个体有13+13+13+13*50个基因[[1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 84, 56, 90, 7, 83, 7, 70, 79, 83, 2, 25, 6, 12, 41, 55, 11, 37, 36, 22, 30, 60, 53, 26, 42, 76, 9, 0.2833708041723647, 0.7035236449013784, 0.7388995125388688, 0.48601576190786533, 0.2748826981715806, 0.039950877158715925, 0.16036818198567426, 0.19710310842524026, 0.7468459749469089, 0.7499767014939938, 0.4290164784457722, 0.7110683048051443, 0.25738837207037624, 0.5034051868008438, 0.5300125512194759, 0.3368547865639494, 0.3135944910640064, 0.2054372472223679, 0.4696002890084563, 0.7895281584830338, 0.18325766723807801, 0.7164713007967947, 0.43747370323031554, 0.050020773994953194, 0.8082241069163905, 0.37542308615545583, 0.4146978484294387, 0.5247658633386343, 0.733292481320159, 0.07597589357651602, 0.3423119969657896, 0.612209476356749, 0.6466737500712026, 0.7224583133191287, 0.8600667515690195, 0.42370752188767247, 0.14067462161252697, 0.009895832604997601, 0.685064045850556, 0.01202891386376026, 0.576184260407531, 0.2736271580417231, 0.5155178373835586, 0.9600945082477527, 0.20759515228684877, 0.47488272867104453, 0.6786065039244358, 0.06683235252297293, 0.07343563629591787, 0.9140581062911858, 0.3189679010422809, 0.334561487032055, 0.24672100999091584, 0.8123972054912327, 0.06558884769196227, 0.5529918690345262, 0.6037244411282147, 0.716051115525277, 0.4336743257702663, 0.16325396014255422, 0.054993588524897996, 0.1279936568538298, 0.0240161168337365, 0.5799055931478346, 0.7838885304900828, 0.2293742318247417, 0.4970616236747808, 0.708787962632707, 0.2860046515895671, 0.3084265738857386, 0.23452181073409661, 0.8108243035763218, 0.5634669774839416, 0.5689526536096937, 0.993344116121792, 0.623443291404663, 0.21687371367745545, 0.757031487958901, 0.610180159800997, 0.561174063498708, 0.8328924019129954, 0.17419577342823245, 0.028017318190715956, 0.477502301320906, 0.16141081503780264, 0.8830647968934745, 0.09534133918285947, 0.7866518624938906, 0.9934221140725785, 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