AllTestSuccessfulRate.py

from cmath import inf, sqrt
from numbers import Integral
import os
from sqlalchemy import true
# from pickle import INT  
import xlwings as xw
import GlobalResource
# import GlobalWorkflow


from sys import maxsize
import matplotlib
import matplotlib.pyplot as plt
from math import trunc

from matplotlib.pyplot import close  

from Class.SyntheticGenerator import SyntheticGenerator
import numpy as np
from Class.VMType import PrivateCloudVMType
from Class.File import File
from Class.Task import Task
import math,random
import copy,re,operator
import time
from tqdm import tqdm

# import pygmo #as pg

from pymoo.indicators.hv import HV as HyperVolume 
from pymoo.indicators.igd_plus import IGDPlus
from pymoo.indicators.igd import IGD

''' 评价算法的非支配解集  '''
class ObjectivesNopermutation:
    def __init__(self):
        self.Cost = None #{'Cost':None}
        self.Cmax= None
        self.Energy = None
        self.TotalTardiness = None
        self.NumPBVMs = None
		# ret = re.findall(r"[^\W_]+",MSIAfileName[n])  #  忽略下划线的正则表达式
		# print(ret)

def DetermineWhether2Dominate(salpA,salpB): ## Cost   Energy   TotalTardiness
    ''' salpA 可支配 salpB  '''
    if ((salpA.Cost<=salpB.Cost) and(salpA.Energy<=salpB.Energy)
            and(salpA.TotalTardiness<=salpB.TotalTardiness)):
        if ((salpA.Cost<salpB.Cost) or (salpA.Energy<salpB.Energy)
            or (salpA.TotalTardiness<salpB.TotalTardiness)):
            return True
    return False

def non_dominatedSalps(SalpPopulation):
    def DetermineWhether2Equal(salpA,salpB): ## Cost   Energy   TotalTardiness
        ''' salpA 等于 salpB  '''
        if ((salpA.Cost==salpB.Cost) and(salpA.Energy==salpB.Energy)
                and(salpA.TotalTardiness==salpB.TotalTardiness)):
            return True  # 包含相等的
        return False
    NonDominSalps = []
    for i in range(len(SalpPopulation)):
        b_non_dominated = True
        for j in range(len(SalpPopulation)):
            if (j!=i) and DetermineWhether2Dominate(SalpPopulation[j],SalpPopulation[i]):
                b_non_dominated = False
                break
        if b_non_dominated:
            NonDominSalps.append(SalpPopulation[i])  
    NonDominSalps.sort(key=operator.attrgetter('Energy','Cost'))
    kk= 0
    while True:
        i = kk +1 
        if DetermineWhether2Equal(NonDominSalps[i],NonDominSalps[kk]):
            NonDominSalps.pop(i)
        else:
            kk += 1
        if kk>=len(NonDominSalps)-1:
            break
    return NonDominSalps

def Normalization_NF(NF,maxC,maxT,maxE,minC,minT,minE):
    def Normalization(x,min,max):
        return (x-min)/(max-min)    
    NF_List = []
    for i in range(len(NF)):
        if (not isinstance(NF[i],dict))and(not isinstance(NF[i],float)) :
            C = Normalization(NF[i].Cost,minC,maxC)
            T = Normalization(NF[i].TotalTardiness,minT,maxT)
            E = Normalization(NF[i].Energy,minE,maxE)
            NF_List.append([C,T,E])
    return NF_List

def InvertedGenerationalDistance(NF,TruePF_List):
    IGD = 0
    for y in TruePF_List:
        dis = inf
        for x in NF:     
            dis = min(dis, np.sqrt(pow(x[1]-y[1],2)+pow(x[2]-y[2],2)+pow(x[0]-y[0],2)))
        IGD = IGD + dis
    return IGD/len(TruePF_List)

def InvertedGenerationalDistancePlus(NF,TruePF_List):
    IGDPlus = 0
    for y in TruePF_List:
        dis = inf
        for x in NF:     
            dis = min(dis, np.sqrt(pow(max(x[1]-y[1],0),2)+pow(max(x[2]-y[2],0),2)+pow(max(x[0]-y[0],0),2)))
        IGDPlus = IGDPlus + dis
    return IGDPlus/len(TruePF_List)

def CoverageRate(A,B):
    '''
    [1] Pan L, Liu X, Jia Z, et al. A Multi-objective Clustering Evolutionary Algorithm for Multi-workflow 
        Computation Offloading in Mobile Edge Computing[J]. IEEE Transactions on Cloud Computing, 2021, 14(8).
    [1] C. ge Wu and L. Wang, “A multi-model estimation of distribution algorithm for energy efficient scheduling 
    under cloud computing system,” J. Parallel Distrib. Comput., vol. 117, pp. 63–72, 2018, doi: 10.1016/j.jpdc.2018.02.009.
    '''
    CR = 0
    for salpB in B:
        for salpA in A: 
            if (not isinstance(salpA,dict)) and(not isinstance(salpB,dict) ) and (not isinstance(salpA,float)) and(not isinstance(salpB,float) ):       
                if DetermineWhether2Dominate(salpA,salpB):
                    CR += 1
                    break
    return CR/len(B)

def DelYears(fileName):
    new = []
    for each in fileName:
        new.append(each[each.index('-')+1:-4])
    return new



############################################################################################
KPI_Name = ['MACO_HV','GMPSO_HV','GALCS_HV','MSSA1_HV','MSSA2_HV','HSM_HV','MSIA_HV',
            'MSIA_MACO','MACO_MSIA','MSIA_GMPSO','GMPSO_MSIA','MSIA_GALCS','GALCS_MSIA',
            'MSIA_MSSA1','MSSA1_MSIA','MSIA_MSSA2','MSSA2_MSIA',
            'HSM_MACO','MACO_HSM','HSM_GMPSO','GMPSO_HSM','HSM_GALCS','GALCS_HSM', 
            'HSM_MSSA1','MSSA1_HSM','HSM_MSSA2','MSSA2_HSM',
            'MSIA_HSM','HSM_MSIA',
            'MACO_RunTime','GMPSO_RunTime','GALCS_RunTime','MSSA1_RunTime','MSSA2_RunTime','HSM_RunTime','MSIA_RunTime',
            'MACO_AvgPBVMs','GMPSO_AvgPBVMs','GALCS_AvgPBVMs','MSSA1_AvgPBVMs','MSSA2_AvgPBVMs','HSM_AvgPBVMs','MSIA_AvgPBVMs',
            'MACO_IGD','GMPSO_IGD','GALCS_IGD','MSSA1_IGD','MSSA2_IGD','HSM_IGD','MSIA_IGD'
            ]
############################################################################################            
AvgBoolean = False  #    True   #   

############################################################################################

if AvgBoolean:
    '''求均值'''    
    app = xw.App(visible=True,add_book=True)
    book = app.books.open(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\test-Revise.xlsx')
    sheet_Original = book.sheets['Sheet1']  #引用工作表 Sheet3-HSM
    sheet = book.sheets.add("TableFig")  # # book.sheets["TableFig"] #   
    n = 1

    for i in range(len(KPI_Name)+1):
        sheet[0,i].value = sheet_Original[0,i].value    
    ni = 1
    while sheet_Original[ni,0].value != None:
        name = sheet_Original[ni,0].value[:-2]
        list_i = []    
        while True:
            if (sheet_Original[ni,0].value == None)or(name!=sheet_Original[ni,0].value[:-2]):
                break
            else:
                list_i.append(ni)
                ni += 1
        ret = re.findall(r"[^\W_]+",name)
        sheet[n,0].value = ret[1]+'_'+ret[2]

        for each in range(1,len(KPI_Name)+1):
            value = []
            if (sheet_Original[list_i[0],each].value ==' \ ')or(sheet_Original[list_i[0],each].value ==None):
                sheet[n,each].value = ' \ '
            else:
                for i1 in list_i:
                    value.append(sheet_Original[i1,each].value) # 
                if ((each>=1+KPI_Name.index('MACO_AvgPBVMs')) and(each<=1+KPI_Name.index('MSIA_AvgPBVMs'))) :
                    sheet[n,each].value = math.ceil(np.average(value)) 
                else:
                    kk = '%.3f'%np.average(value)
                    sheet[n,each].value = np.average(value) # kk # round(np.average(value),3)
                    sheet[n,each].api.NumberFormat = '0.000' # round(np.average(value),3)
        
        ''' 优值标注'''      
        AlgorithmNumbers = 7
        '''max'''
        HV_List = [sheet[n, j+1].value for j in range(AlgorithmNumbers)]
        HV_List[0] = -inf if HV_List[0]== ' \ ' else  HV_List[0]
        max2 = sorted(HV_List)[AlgorithmNumbers-2]
        for j in range(AlgorithmNumbers):
            if sheet[n, j+1].value == max(HV_List):
                sheet[n, j+1].api.Font.Color = 0x0000ff #red
                sheet[n, j+1].api.Font.Bold = True
                # sheet[n, j+1].api.Font.Italic = True
                sheet[n, j+1].api.Font.Underline = True
            if sheet[n, j+1].value == max2:
                # sheet[n, j+1].api.Font.Color = 0x0000ff #red
                sheet[n, j+1].api.Font.Bold = True
                sheet[n, j+1].api.Font.Italic = True 
        
        
        CR_Num = (AlgorithmNumbers-2)*2+1
        start_num = KPI_Name.index('MSIA_MACO')
        for each in range(CR_Num):
            if sheet[n, start_num+1+each*2].value > sheet[n, start_num+1+1+each*2].value:
                # sheet[n, start_num+1+each*2].api.Font.Color = 0x0000ff
                sheet[n, start_num+1+each*2].api.Font.Bold = True
                # sheet[n, start_num+1+each*2].api.Font.Italic = True            
            elif sheet[n, start_num+1+each*2].value < sheet[n, start_num+1+1+each*2].value:
                # sheet[n, start_num+1+1+each*2].api.Font.Color = 0x0000ff
                sheet[n, start_num+1+1+each*2].api.Font.Bold = True
                # sheet[n, start_num+1+1+each*2].api.Font.Italic = True
        '''min'''
        minNum = 3  #  3中情况：运行时间 、公有云资源的个数、IGD
        start_num = KPI_Name.index('MACO_RunTime')
        for each in [0]:# range(minNum) ## 运行时间 第二小的加粗
            HV_List = [sheet[n, start_num+1+j+each*AlgorithmNumbers].value for j in range(AlgorithmNumbers)]
            HV_List[0] = inf if HV_List[0]== ' \ ' else  HV_List[0]
            min2 = sorted(HV_List)[1]
            for j in range(AlgorithmNumbers):
                if HV_List[j] == min(HV_List):
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Color = 0x0000ff #red
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Bold = True  
                    # sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Italic = True
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Underline = True 
                if HV_List[j] == min2:
                    # sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Color = 0x0000ff #red
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Bold = True  
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Italic = True
                    # sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Underline = 'doubleAccounting'

        for each in range(1,minNum):
            HV_List = [sheet[n, start_num+1+j+each*AlgorithmNumbers].value for j in range(AlgorithmNumbers)]
            HV_List[0] = inf if HV_List[0]== ' \ ' else  HV_List[0]
            min2 = sorted(HV_List)[1]
            for j in range(AlgorithmNumbers):
                if HV_List[j] == min(HV_List):
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Color = 0x0000ff #red
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Bold = True  
                    # sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Italic = True 
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Underline = True
                if HV_List[j] == min2:
                    # sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Color = 0x0000ff #red
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Bold = True  
                    sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Italic = True 
                    # sheet[n, start_num+1+j+each*AlgorithmNumbers].api.Font.Underline = 'doubleAccounting'               
            
            
        n += 1
    book.save()
    book.close()
    app.quit()       

############################################################################################


readData_Output = True   #     False   #
############################################################################################
if readData_Output:
    '''读数据输出PF'''

    MSIAfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSIA')
    # MSIAfileName.sort()
    MACOfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MACO')
    # MACOfileName.sort()
    GMPSOfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GMPSO')
    # GMPSOfileName.sort()
    GALCSfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GALCS')
    # GALCSfileName.sort()
    HSMfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\HSM')
    # HSMfileName.sort()  
    MSSA1fileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA')
    MSSA2fileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA_EFT')
   
    MSIAfileName_NoYears = DelYears(MSIAfileName)
    MACOfileName_NoYears = DelYears(MACOfileName)
    GMPSOfileName_NoYears = DelYears(GMPSOfileName)
    GALCSfileName_NoYears = DelYears(GALCSfileName)
    HSMfileName_NoYears = DelYears(HSMfileName)
    MSSA1fileName_NoYears = DelYears(MSSA1fileName)
    MSSA2fileName_NoYears = DelYears(MSSA2fileName)

    Labels= ['0_3_177_0', '0_3_177_1','0_3_177_2','0_3_177_3','0_3_177_4',
            '0_3_177_5','0_3_177_6','0_3_177_7','0_3_177_8','0_3_177_9',
            '1_3_200_0','1_3_200_1','1_3_200_2','1_3_200_3','1_3_200_4',
            '1_3_200_5','1_3_200_6','1_3_200_7','1_3_200_8','1_3_200_9',
            '2_5_321_0','2_5_321_1','2_5_321_2','2_5_321_3','2_5_321_4',
            '2_5_321_5','2_5_321_6','2_5_321_7','2_5_321_8','2_5_321_9',
            '3_5_429_0','3_5_429_1','3_5_429_2','3_5_429_3','3_5_429_4',
            '3_5_429_5','3_5_429_6','3_5_429_7','3_5_429_8','3_5_429_9',
            '4_7_390_0','4_7_390_1','4_7_390_2','4_7_390_3','4_7_390_4',
            '4_7_390_5','4_7_390_6','4_7_390_7','4_7_390_8','4_7_390_9',
            '5_7_392_0','5_7_392_1','5_7_392_2','5_7_392_3','5_7_392_4',
            '5_7_392_5','5_7_392_6','5_7_392_7','5_7_392_8','5_7_392_9',
            '6_10_649_0','6_10_649_1','6_10_649_2','6_10_649_3','6_10_649_4',
            '6_10_649_5','6_10_649_6','6_10_649_7','6_10_649_8','6_10_649_9',
            '7_10_671_0','7_10_671_1','7_10_671_2','7_10_671_3','7_10_671_4',
            '7_10_671_5','7_10_671_6','7_10_671_7','7_10_671_8','7_10_671_9',
            '8_5_1229_0','8_5_1229_1','8_5_1229_2','8_5_1229_3','8_5_1229_4',
            '8_5_1229_5','8_5_1229_6','8_5_1229_7','8_5_1229_8','8_5_1229_9',
            '9_5_1300_0','9_5_1300_1','9_5_1300_2','9_5_1300_3','9_5_1300_4',
            '9_5_1300_5','9_5_1300_6','9_5_1300_7','9_5_1300_8','9_5_1300_9',
            '10_5_3150_0','10_5_3150_1','10_5_3150_2','10_5_3150_3','10_5_3150_4',
            '10_5_3150_5','10_5_3150_6','10_5_3150_7','10_5_3150_8','10_5_3150_9',
            '11_15_2653_0','11_15_2653_1','11_15_2653_2','11_15_2653_3','11_15_2653_4',
            '11_15_2653_5','11_15_2653_6','11_15_2653_7','11_15_2653_8','11_15_2653_9',
            '12_15_4646_0','12_15_4646_1','12_15_4646_2','12_15_4646_3','12_15_4646_4',
            '12_15_4646_5','12_15_4646_6','12_15_4646_7','12_15_4646_8','12_15_4646_9',
            '13_15_5520_0','13_15_5520_1','13_15_5520_2','13_15_5520_3','13_15_5520_4',
            '13_15_5520_5','13_15_5520_6','13_15_5520_7','13_15_5520_8','13_15_5520_9',
            '14_25_6097_0','14_25_6097_1','14_25_6097_2','14_25_6097_3','14_25_6097_4',
            '14_25_6097_5','14_25_6097_6','14_25_6097_7','14_25_6097_8','14_25_6097_9',
            '15_25_7880_0','15_25_7880_1','15_25_7880_2','15_25_7880_3','15_25_7880_4',
            '15_25_7880_5','15_25_7880_6','15_25_7880_7','15_25_7880_8','15_25_7880_9',
            '16_25_10816_0','16_25_10816_1','16_25_10816_2','16_25_10816_3','16_25_10816_4',
            '16_25_10816_5','16_25_10816_6','16_25_10816_7','16_25_10816_8','16_25_10816_9']




    # app = xw.App(visible=True, add_book=False)
    # app.display_alerts = False    # 关闭一些提示信息，可以加快运行速度。 默认为 True。
    # app.screen_updating = True    # 更新显示工作表的内容。默认为 True。关闭它也可以提升运行速度。
    # book = app.books.add()
    # # book = app.books.open(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\test.xlsx')
    # sheet = book.sheets.active

    n = 0
    C0,C1,C2,C3 = 0,1,2,3
    for n_ in tqdm(range(len(Labels))):        
        time.sleep(0.1)
        n0 = MSIAfileName_NoYears.index(Labels[n_])
        if ((Labels[n_]in MACOfileName_NoYears) and (Labels[n_]in GMPSOfileName_NoYears) 
            and(Labels[n_]in GALCSfileName_NoYears)):

            n1 = MACOfileName_NoYears.index(Labels[n_])
            n2 = GMPSOfileName_NoYears.index(Labels[n_])
            n3 = GALCSfileName_NoYears.index(Labels[n_])
            n4 = HSMfileName_NoYears.index(Labels[n_])
            n5 = MSSA1fileName_NoYears.index(Labels[n_])
            n6 = MSSA2fileName_NoYears.index(Labels[n_])            
            if true:
                MSIA = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSIA'+'\\'+MSIAfileName[n0],allow_pickle=True)
                MACO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MACO'+'\\'+MACOfileName[n1],allow_pickle=True)
                GMPSO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GMPSO'+'\\'+GMPSOfileName[n2],allow_pickle=True)
                GALCS = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GALCS'+'\\'+GALCSfileName[n3],allow_pickle=True)
                HSM = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\HSM'+'\\'+HSMfileName[n4],allow_pickle=True)
                MSSA1 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA'+'\\'+MSSA1fileName[n4],allow_pickle=True)
                MSSA2 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA_EFT'+'\\'+MSSA2fileName[n4],allow_pickle=True)
                # succ = 0
                # for i in range(len(MSIA)-1):
                #     if succ<MSIA[i].SuccessfulRate:
                #         succ = MSIA[i].SuccessfulRate
                # print('MSIA',Labels[n_+10*n],succ)
                kk = 0
                sheet[kk,n*4+C0].value= Labels[n_] 
                sheet[kk,n*4+C1].value= 'Cost'
                sheet[kk,n*4+C2].value= 'TotalTardiness'
                sheet[kk,n*4+C3].value= 'Energy' 
                kk += 1
                for i in range(len(MACO)-1):
                    sheet[kk,n*4+C0].value= 'MACO'  
                    sheet[kk,n*4+C1].value= MACO[i].Cost
                    sheet[kk,n*4+C2].value= MACO[i].TotalTardiness
                    sheet[kk,n*4+C3].value= MACO[i].Energy 
                    kk += 1
                for i in range(len(GMPSO)-1):
                    sheet[kk,n*4+C0].value= 'GMPSO'  
                    sheet[kk,n*4+C1].value= GMPSO[i].Cost
                    sheet[kk,n*4+C2].value= GMPSO[i].TotalTardiness
                    sheet[kk,n*4+C3].value= GMPSO[i].Energy 
                    kk += 1
                for i in range(len(GALCS)-1):
                    sheet[kk,n*4+C0].value= 'GALCS'  
                    sheet[kk,n*4+C1].value= GALCS[i].Cost
                    sheet[kk,n*4+C2].value= GALCS[i].TotalTardiness
                    sheet[kk,n*4+C3].value= GALCS[i].Energy 
                    kk += 1
                for i in range(len(MSSA1)-1):
                    sheet[kk,n*4+C0].value= 'MSSA1'  
                    sheet[kk,n*4+C1].value= MSSA1[i].Cost
                    sheet[kk,n*4+C2].value= MSSA1[i].TotalTardiness
                    sheet[kk,n*4+C3].value= MSSA1[i].Energy 
                    kk += 1
                for i in range(len(MSSA2)-1):
                    sheet[kk,n*4+C0].value= 'MSSA2'  
                    sheet[kk,n*4+C1].value= MSSA2[i].Cost
                    sheet[kk,n*4+C2].value= MSSA2[i].TotalTardiness
                    sheet[kk,n*4+C3].value= MSSA2[i].Energy 
                    kk += 1                                        
                for i in range(len(HSM)-1):
                    sheet[kk,n*4+C0].value= 'HSA9Fs'  
                    sheet[kk,n*4+C1].value= HSM[i].Cost
                    sheet[kk,n*4+C2].value= HSM[i].TotalTardiness
                    sheet[kk,n*4+C3].value= HSM[i].Energy 
                    kk += 1
                for i in range(len(MSIA)-1):
                    sheet[kk,n*4+C0].value= 'MSIA'  
                    sheet[kk,n*4+C1].value= MSIA[i].Cost
                    sheet[kk,n*4+C2].value= MSIA[i].TotalTardiness
                    sheet[kk,n*4+C3].value= MSIA[i].Energy 
                    kk += 1

        else:
            ret = re.findall(r"[^\W_]+",MSIAfileName[n0])  #  忽略下划线的正则表达式
            if int(ret[5])>1000:
                if ((Labels[n_]in GMPSOfileName_NoYears) and(Labels[n_]in GALCSfileName_NoYears)):
                    # n1 = MACOfileName_NoYears.index(Labels[n_])
                    n2 = GMPSOfileName_NoYears.index(Labels[n_])
                    n3 = GALCSfileName_NoYears.index(Labels[n_])
                    n4 = HSMfileName_NoYears.index(Labels[n_])
                    n5 = MSSA1fileName_NoYears.index(Labels[n_])
                    n6 = MSSA2fileName_NoYears.index(Labels[n_])
                    if true:
                        MSIA = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSIA'+'\\'+MSIAfileName[n0],allow_pickle=True)
                        # MACO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MACO'+'\\'+MACOfileName[n1],allow_pickle=True)
                        GMPSO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GMPSO'+'\\'+GMPSOfileName[n2],allow_pickle=True)
                        GALCS = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GALCS'+'\\'+GALCSfileName[n3],allow_pickle=True)
                        HSM = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\HSM'+'\\'+HSMfileName[n4],allow_pickle=True)
                        MSSA1 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA'+'\\'+MSSA1fileName[n4],allow_pickle=True)
                        MSSA2 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA_EFT'+'\\'+MSSA2fileName[n4],allow_pickle=True)

                        kk = 0
                        sheet[kk,n*4+C0].value= Labels[n_] 
                        sheet[kk,n*4+C1].value= 'Cost'
                        sheet[kk,n*4+C2].value= 'TotalTardiness'
                        sheet[kk,n*4+C3].value= 'Energy' 
                        kk += 1                        
                        # for i in range(len(MACO)-1):
                        #     sheet[kk,n*4+C0].value= 'MACO'  
                        #     sheet[kk,n*4+C1].value= MACO[i].Cost
                        #     sheet[kk,n*4+C2].value= MACO[i].TotalTardiness
                        #     sheet[kk,n*4+C3].value= MACO[i].Energy 
                        #     kk += 1
                        for i in range(len(GMPSO)-1):
                            sheet[kk,n*4+C0].value= 'GMPSO'  
                            sheet[kk,n*4+C1].value= GMPSO[i].Cost
                            sheet[kk,n*4+C2].value= GMPSO[i].TotalTardiness
                            sheet[kk,n*4+C3].value= GMPSO[i].Energy 
                            kk += 1
                        for i in range(len(GALCS)-1):
                            sheet[kk,n*4+C0].value= 'GALCS'  
                            sheet[kk,n*4+C1].value= GALCS[i].Cost
                            sheet[kk,n*4+C2].value= GALCS[i].TotalTardiness
                            sheet[kk,n*4+C3].value= GALCS[i].Energy 
                            kk += 1
                        for i in range(len(MSSA1)-1):
                            sheet[kk,n*4+C0].value= 'MSSA1'  
                            sheet[kk,n*4+C1].value= MSSA1[i].Cost
                            sheet[kk,n*4+C2].value= MSSA1[i].TotalTardiness
                            sheet[kk,n*4+C3].value= MSSA1[i].Energy 
                            kk += 1
                        for i in range(len(MSSA2)-1):
                            sheet[kk,n*4+C0].value= 'MSSA2'  
                            sheet[kk,n*4+C1].value= MSSA2[i].Cost
                            sheet[kk,n*4+C2].value= MSSA2[i].TotalTardiness
                            sheet[kk,n*4+C3].value= MSSA2[i].Energy 
                            kk += 1                                            
                        for i in range(len(HSM)-1):
                            sheet[kk,n*4+C0].value= 'HSA9Fs'  
                            sheet[kk,n*4+C1].value= HSM[i].Cost
                            sheet[kk,n*4+C2].value= HSM[i].TotalTardiness
                            sheet[kk,n*4+C3].value= HSM[i].Energy 
                            kk += 1
                        for i in range(len(MSIA)-1):
                            sheet[kk,n*4+C0].value= 'MSIA'  
                            sheet[kk,n*4+C1].value= MSIA[i].Cost
                            sheet[kk,n*4+C2].value= MSIA[i].TotalTardiness
                            sheet[kk,n*4+C3].value= MSIA[i].Energy 
                            kk += 1
        n += 1



    
############################################################################################

# AlgorithmNumbers = 5
readData_Output = False   #   True   #  
############################################################################################
if readData_Output:
    '''读数据输出评价结果'''

    # KPI_Name = ['MACO_HV','GMPSO_HV','GALCS_HV','HSM_HV','MSIA_HV',
    #             'MSIA_MACO','MACO_MSIA','MSIA_GMPSO','GMPSO_MSIA','MSIA_GALCS','GALCS_MSIA',
    #             'HSM_MACO','MACO_HSM','HSM_GMPSO','GMPSO_HSM','HSM_GALCS','GALCS_HSM', 'MSIA_HSM','HSM_MSIA',
    #             'MACO_RunTime','GMPSO_RunTime','GALCS_RunTime','HSM_RunTime','MSIA_RunTime',
    #             'MACO_AvgPBVMs','GMPSO_AvgPBVMs','GALCS_AvgPBVMs','HSM_AvgPBVMs','MSIA_AvgPBVMs',
    #             'MACO_IGD','GMPSO_IGD','GALCS_IGD','HSM_IGD','MSIA_IGD'
    #             ]


    MSIAfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSIA')
    # MSIAfileName.sort()
    MACOfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MACO')
    # MACOfileName.sort()
    GMPSOfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GMPSO')
    # GMPSOfileName.sort()
    GALCSfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GALCS')
    # GALCSfileName.sort()
    HSMfileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\HSM')
    # HSMfileName.sort()    
    MSSA1fileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA')
    MSSA2fileName=os.listdir(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA_EFT')

    MSIAfileName_NoYears = DelYears(MSIAfileName)
    MACOfileName_NoYears = DelYears(MACOfileName)
    GMPSOfileName_NoYears = DelYears(GMPSOfileName)
    GALCSfileName_NoYears = DelYears(GALCSfileName)
    HSMfileName_NoYears = DelYears(HSMfileName)
    MSSA1fileName_NoYears = DelYears(MSSA1fileName)
    MSSA2fileName_NoYears = DelYears(MSSA2fileName)

    Labels= ['0_3_177_0', '0_3_177_1','0_3_177_2','0_3_177_3','0_3_177_4',
            '0_3_177_5','0_3_177_6','0_3_177_7','0_3_177_8','0_3_177_9',
            '1_3_200_0','1_3_200_1','1_3_200_2','1_3_200_3','1_3_200_4',
            '1_3_200_5','1_3_200_6','1_3_200_7','1_3_200_8','1_3_200_9',
            '2_5_321_0','2_5_321_1','2_5_321_2','2_5_321_3','2_5_321_4',
            '2_5_321_5','2_5_321_6','2_5_321_7','2_5_321_8','2_5_321_9',
            '3_5_429_0','3_5_429_1','3_5_429_2','3_5_429_3','3_5_429_4',
            '3_5_429_5','3_5_429_6','3_5_429_7','3_5_429_8','3_5_429_9',
            '4_7_390_0','4_7_390_1','4_7_390_2','4_7_390_3','4_7_390_4',
            '4_7_390_5','4_7_390_6','4_7_390_7','4_7_390_8','4_7_390_9',
            '5_7_392_0','5_7_392_1','5_7_392_2','5_7_392_3','5_7_392_4',
            '5_7_392_5','5_7_392_6','5_7_392_7','5_7_392_8','5_7_392_9',
            '6_10_649_0','6_10_649_1','6_10_649_2','6_10_649_3','6_10_649_4',
            '6_10_649_5','6_10_649_6','6_10_649_7','6_10_649_8','6_10_649_9',
            '7_10_671_0','7_10_671_1','7_10_671_2','7_10_671_3','7_10_671_4',
            '7_10_671_5','7_10_671_6','7_10_671_7','7_10_671_8','7_10_671_9',
            '8_5_1229_0','8_5_1229_1','8_5_1229_2','8_5_1229_3','8_5_1229_4',
            '8_5_1229_5','8_5_1229_6','8_5_1229_7','8_5_1229_8','8_5_1229_9',
            '9_5_1300_0','9_5_1300_1','9_5_1300_2','9_5_1300_3','9_5_1300_4',
            '9_5_1300_5','9_5_1300_6','9_5_1300_7','9_5_1300_8','9_5_1300_9',
            '10_5_3150_0','10_5_3150_1','10_5_3150_2','10_5_3150_3','10_5_3150_4',
            '10_5_3150_5','10_5_3150_6','10_5_3150_7','10_5_3150_8','10_5_3150_9',
            '11_15_2653_0','11_15_2653_1','11_15_2653_2','11_15_2653_3','11_15_2653_4',
            '11_15_2653_5','11_15_2653_6','11_15_2653_7','11_15_2653_8','11_15_2653_9',
            '12_15_4646_0','12_15_4646_1','12_15_4646_2','12_15_4646_3','12_15_4646_4',
            '12_15_4646_5','12_15_4646_6','12_15_4646_7','12_15_4646_8','12_15_4646_9',
            '13_15_5520_0','13_15_5520_1','13_15_5520_2','13_15_5520_3','13_15_5520_4',
            '13_15_5520_5','13_15_5520_6','13_15_5520_7','13_15_5520_8','13_15_5520_9',
            '14_25_6097_0','14_25_6097_1','14_25_6097_2','14_25_6097_3','14_25_6097_4',
            '14_25_6097_5','14_25_6097_6','14_25_6097_7','14_25_6097_8','14_25_6097_9',
            '15_25_7880_0','15_25_7880_1','15_25_7880_2','15_25_7880_3','15_25_7880_4',
            '15_25_7880_5','15_25_7880_6','15_25_7880_7','15_25_7880_8','15_25_7880_9',
            '16_25_10816_0','16_25_10816_1','16_25_10816_2','16_25_10816_3','16_25_10816_4',
            '16_25_10816_5','16_25_10816_6','16_25_10816_7','16_25_10816_8','16_25_10816_9']



    app = xw.App(visible=True, add_book=False)
    app.display_alerts = False    # 关闭一些提示信息，可以加快运行速度。 默认为 True。
    app.screen_updating = True    # 更新显示工作表的内容。默认为 True。关闭它也可以提升运行速度。
    book = app.books.add()
    # book = app.books.open(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\test.xlsx')
    sheet = book.sheets.active


    
    sheet[0, 0].value= '算法测试'	# 向表test中添加数据
    for i in range(len(KPI_Name)):
        sheet[0, i+1].value= KPI_Name[i]

    n = 0
    for n_ in tqdm(range(len(Labels))):        
        time.sleep(0.1)
        n0 = MSIAfileName_NoYears.index(Labels[n_])
        if ((Labels[n_]in MACOfileName_NoYears) and (Labels[n_]in GMPSOfileName_NoYears) 
            and(Labels[n_]in GALCSfileName_NoYears)):

            n1 = MACOfileName_NoYears.index(Labels[n_])
            n2 = GMPSOfileName_NoYears.index(Labels[n_])
            n3 = GALCSfileName_NoYears.index(Labels[n_])
            n4 = HSMfileName_NoYears.index(Labels[n_])
            n5 = MSSA1fileName_NoYears.index(Labels[n_])
            n6 = MSSA2fileName_NoYears.index(Labels[n_])
            if true:
                MSIA = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSIA'+'\\'+MSIAfileName[n0],allow_pickle=True)
                MACO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MACO'+'\\'+MACOfileName[n1],allow_pickle=True)
                GMPSO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GMPSO'+'\\'+GMPSOfileName[n2],allow_pickle=True)
                GALCS = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GALCS'+'\\'+GALCSfileName[n3],allow_pickle=True)
                HSM = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\HSM'+'\\'+HSMfileName[n4],allow_pickle=True)
                MSSA1 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA'+'\\'+MSSA1fileName[n4],allow_pickle=True)
                MSSA2 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA_EFT'+'\\'+MSSA2fileName[n4],allow_pickle=True)

                AllNF = []
                maxC,maxT,maxE = 0,0,0
                minC,minT,minE = inf,inf,inf
                for i in range(len(MSIA)-1):
                    AllNF.append(MSIA[i])
                for i in range(len(MACO)-1):
                    AllNF.append(MACO[i])
                for i in range(len(GMPSO)-1):
                    AllNF.append(GMPSO[i])
                for i in range(len(GALCS)-1):
                    AllNF.append(GALCS[i])
                for i in range(len(MSSA1)-1):
                    AllNF.append(MSSA1[i])
                for i in range(len(MSSA2)-1):
                    AllNF.append(MSSA2[i])                                        
                # for i in range(len(HSM)-1):
                AllNF.append(HSM[0])
                for each in AllNF:
                    maxC,maxT,maxE = max(each.Cost,maxC),max(each.TotalTardiness,maxT),max(each.Energy,maxE)
                    minC,minT,minE = min(each.Cost,minC),min(each.TotalTardiness,minT),min(each.Energy,minE)

                MSIA_List = sorted(Normalization_NF(MSIA,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                MACO_List = sorted(Normalization_NF(MACO,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                GMPSO_List = sorted(Normalization_NF(GMPSO,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                GALCS_List = sorted(Normalization_NF(GALCS,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                MSSA1_List = sorted(Normalization_NF(MSSA1,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                MSSA2_List = sorted(Normalization_NF(MSSA2,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))                
                HSM_List = sorted(Normalization_NF(HSM,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                AllNF_List = sorted(Normalization_NF(AllNF,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                TruePF = non_dominatedSalps(AllNF)
                TruePF_List = sorted(Normalization_NF(TruePF,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))


                # MSIA_IGDPlus = InvertedGenerationalDistancePlus(MSIA_List,TruePF_List)
                # MACO_IGDPlus = InvertedGenerationalDistancePlus(MACO_List,TruePF_List)
                # GMPSO_IGDPlus = InvertedGenerationalDistancePlus(GMPSO_List,TruePF_List)
                # GALCS_IGDPlus = InvertedGenerationalDistancePlus(GALCS_List,TruePF_List)
                ReferNF_List = [1,1,1]        
                hv = HyperVolume(ref_point = ReferNF_List)
                KPI = {}
                KPI['MSIA_HV'] = hv.do(np.array(MSIA_List))
                KPI['MACO_HV'] = hv.do(np.array(MACO_List))
                KPI['GMPSO_HV'] = hv.do(np.array(GMPSO_List))
                KPI['GALCS_HV'] = hv.do(np.array(GALCS_List)) 
                KPI['MSSA1_HV'] = hv.do(np.array(MSSA1_List)) 
                KPI['MSSA2_HV'] = hv.do(np.array(MSSA2_List)) 
                KPI['HSM_HV'] = hv.do(np.array(HSM_List))  

                KPI['MSIA_IGD'] = InvertedGenerationalDistance(MSIA_List,TruePF_List)
                KPI['MACO_IGD'] = InvertedGenerationalDistance(MACO_List,TruePF_List)
                KPI['GMPSO_IGD'] = InvertedGenerationalDistance(GMPSO_List,TruePF_List)
                KPI['GALCS_IGD'] = InvertedGenerationalDistance(GALCS_List,TruePF_List) 
                KPI['HSM_IGD'] = InvertedGenerationalDistance(HSM_List,TruePF_List)     
                KPI['MSSA1_IGD'] = InvertedGenerationalDistance(MSSA1_List,TruePF_List) 
                KPI['MSSA2_IGD'] = InvertedGenerationalDistance(MSSA2_List,TruePF_List)    

                KPI['MSIA_MACO'] = CoverageRate(MSIA,MACO)
                KPI['MACO_MSIA'] = CoverageRate(MACO,MSIA)

                KPI['MSIA_GMPSO'] = CoverageRate(MSIA,GMPSO)
                KPI['GMPSO_MSIA'] = CoverageRate(GMPSO,MSIA)

                KPI['MSIA_GALCS'] = CoverageRate(MSIA,GALCS)
                KPI['GALCS_MSIA'] = CoverageRate(GALCS,MSIA) 

                KPI['MSIA_MSSA1'] = CoverageRate(MSIA,MSSA1)
                KPI['MSSA1_MSIA'] = CoverageRate(MSSA1,MSIA)

                KPI['MSIA_MSSA2'] = CoverageRate(MSIA,MSSA2)
                KPI['MSSA2_MSIA'] = CoverageRate(MSSA2,MSIA)                

                KPI['HSM_MSSA1'] = CoverageRate(HSM,MSSA1)
                KPI['MSSA1_HSM'] = CoverageRate(MSSA1,HSM)

                KPI['HSM_MSSA2'] = CoverageRate(HSM,MSSA2)
                KPI['MSSA2_HSM'] = CoverageRate(MSSA2,HSM)  

                KPI['HSM_MACO'] = CoverageRate(HSM,MACO)
                KPI['MACO_HSM'] = CoverageRate(MACO,HSM)

                KPI['HSM_GMPSO'] = CoverageRate(HSM,GMPSO)
                KPI['GMPSO_HSM'] = CoverageRate(GMPSO,HSM)

                KPI['HSM_GALCS'] = CoverageRate(HSM,GALCS)
                KPI['GALCS_HSM'] = CoverageRate(GALCS,HSM)    

                KPI['MSIA_HSM'] = CoverageRate(MSIA,HSM)
                KPI['HSM_MSIA'] = CoverageRate(HSM,MSIA)               
   
                KPI['MSIA_RunTime']= MSIA[len(MSIA)-1]['RunTime'] 
                KPI['MACO_RunTime']= MACO[len(MACO)-1]['RunTime'] 
                KPI['GMPSO_RunTime']= GMPSO[len(GMPSO)-1]['RunTime']
                KPI['GALCS_RunTime']= GALCS[len(GALCS)-1]['RunTime'] 
                KPI['MSSA1_RunTime']= MSSA1[len(MSSA1)-1]['RunTime'] 
                KPI['MSSA2_RunTime']= MSSA2[len(MSSA2)-1]['RunTime'] 
                KPI['HSM_RunTime']= HSM[len(HSM)-1]['RunTime'] 

                KPI['MSIA_AvgPBVMs']= MSIA[len(MSIA)-1]['AvgPBVMs']
                KPI['MACO_AvgPBVMs']= MACO[len(MACO)-1]['AvgPBVMs']
                KPI['GMPSO_AvgPBVMs']= GMPSO[len(GMPSO)-1]['AvgPBVMs']
                KPI['GALCS_AvgPBVMs']= GALCS[len(GALCS)-1]['AvgPBVMs']
                KPI['MSSA1_AvgPBVMs']= MSSA1[len(MSSA1)-1]['AvgPBVMs']
                KPI['MSSA2_AvgPBVMs']= MSSA2[len(MSSA2)-1]['AvgPBVMs']
                KPI['HSM_AvgPBVMs']= HSM[len(HSM)-1]['AvgPBVMs']
  
        else:
            ret = re.findall(r"[^\W_]+",MSIAfileName[n0])  #  忽略下划线的正则表达式
            if int(ret[5])>1000:
                if ((Labels[n_]in GMPSOfileName_NoYears) and(Labels[n_]in GALCSfileName_NoYears)): 
                        #(Labels[n_]in MACOfileName_NoYears) and 
                    # n1 = MACOfileName_NoYears.index(Labels[n_])
                    n2 = GMPSOfileName_NoYears.index(Labels[n_])
                    n3 = GALCSfileName_NoYears.index(Labels[n_])
                    n4 = HSMfileName_NoYears.index(Labels[n_])
                    n5 = MSSA1fileName_NoYears.index(Labels[n_])
                    n6 = MSSA2fileName_NoYears.index(Labels[n_])                    
                    if true:
                        MSIA = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSIA'+'\\'+MSIAfileName[n0],allow_pickle=True)
                        # MACO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MACO'+'\\'+MACOfileName[n1],allow_pickle=True)
                        GMPSO = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GMPSO'+'\\'+GMPSOfileName[n2],allow_pickle=True)
                        GALCS = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\GALCS'+'\\'+GALCSfileName[n3],allow_pickle=True)
                        HSM = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\HSM'+'\\'+HSMfileName[n4],allow_pickle=True)
                        MSSA1 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA'+'\\'+MSSA1fileName[n4],allow_pickle=True)
                        MSSA2 = np.load(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\MSSA_EFT'+'\\'+MSSA2fileName[n4],allow_pickle=True)

                        AllNF = []
                        maxC,maxT,maxE = 0,0,0
                        minC,minT,minE = inf,inf,inf
                        for i in range(len(MSIA)-1):
                            AllNF.append(MSIA[i])
                        # for i in range(len(MACO)-1):
                        #     AllNF.append(MACO[i])
                        for i in range(len(GMPSO)-1):
                            AllNF.append(GMPSO[i])
                        for i in range(len(GALCS)-1):
                            AllNF.append(GALCS[i])
                        for i in range(len(MSSA1)-1):
                            AllNF.append(MSSA1[i])
                        for i in range(len(MSSA2)-1):
                            AllNF.append(MSSA2[i])                              
                        # for i in range(len(HSM)-1):
                        AllNF.append(HSM[0])
                        for each in AllNF:
                            maxC,maxT,maxE = max(each.Cost,maxC),max(each.TotalTardiness,maxT),max(each.Energy,maxE)
                            minC,minT,minE = min(each.Cost,minC),min(each.TotalTardiness,minT),min(each.Energy,minE)

                        MSIA_List = sorted(Normalization_NF(MSIA,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        # MACO_List = sorted(Normalization_NF(MACO,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        GMPSO_List = sorted(Normalization_NF(GMPSO,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        GALCS_List = sorted(Normalization_NF(GALCS,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        MSSA1_List = sorted(Normalization_NF(MSSA1,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        MSSA2_List = sorted(Normalization_NF(MSSA2,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))                
                        HSM_List = sorted(Normalization_NF(HSM,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        AllNF_List = sorted(Normalization_NF(AllNF,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))
                        TruePF = non_dominatedSalps(AllNF)
                        TruePF_List = sorted(Normalization_NF(TruePF,maxC,maxT,maxE,minC,minT,minE),key=operator.itemgetter(0,2))


                        # MSIA_IGDPlus = InvertedGenerationalDistancePlus(MSIA_List,TruePF_List)
                        # MACO_IGDPlus = InvertedGenerationalDistancePlus(MACO_List,TruePF_List)
                        # GMPSO_IGDPlus = InvertedGenerationalDistancePlus(GMPSO_List,TruePF_List)
                        # GALCS_IGDPlus = InvertedGenerationalDistancePlus(GALCS_List,TruePF_List)
                        ReferNF_List = [1,1,1]        
                        hv = HyperVolume(ref_point = ReferNF_List)
                        KPI = {}
                        KPI['MSIA_HV'] = hv.do(np.array(MSIA_List))
                        KPI['MACO_HV'] = ' \ ' # hv.do(np.array(MACO_List))
                        KPI['GMPSO_HV'] = hv.do(np.array(GMPSO_List))
                        KPI['GALCS_HV'] = hv.do(np.array(GALCS_List)) 
                        KPI['MSSA1_HV'] = hv.do(np.array(MSSA1_List)) 
                        KPI['MSSA2_HV'] = hv.do(np.array(MSSA2_List)) 
                        KPI['HSM_HV'] = hv.do(np.array(HSM_List))  

                        KPI['MSIA_IGD'] = InvertedGenerationalDistance(MSIA_List,TruePF_List)
                        KPI['MACO_IGD'] = ' \ ' # InvertedGenerationalDistance(MACO_List,TruePF_List)
                        KPI['GMPSO_IGD'] = InvertedGenerationalDistance(GMPSO_List,TruePF_List)
                        KPI['GALCS_IGD'] = InvertedGenerationalDistance(GALCS_List,TruePF_List) 
                        KPI['HSM_IGD'] = InvertedGenerationalDistance(HSM_List,TruePF_List)        
                        KPI['MSSA1_IGD'] = InvertedGenerationalDistance(MSSA1_List,TruePF_List) 
                        KPI['MSSA2_IGD'] = InvertedGenerationalDistance(MSSA2_List,TruePF_List)    

                        KPI['MSIA_MACO'] = ' \ ' # CoverageRate(MSIA,MACO)
                        KPI['MACO_MSIA'] = ' \ ' # CoverageRate(MACO,MSIA)

                        KPI['MSIA_GMPSO'] = CoverageRate(MSIA,GMPSO)
                        KPI['GMPSO_MSIA'] = CoverageRate(GMPSO,MSIA)

                        KPI['MSIA_GALCS'] = CoverageRate(MSIA,GALCS)
                        KPI['GALCS_MSIA'] = CoverageRate(GALCS,MSIA) 

                        KPI['MSIA_MSSA1'] = CoverageRate(MSIA,MSSA1)
                        KPI['MSSA1_MSIA'] = CoverageRate(MSSA1,MSIA)

                        KPI['MSIA_MSSA2'] = CoverageRate(MSIA,MSSA2)
                        KPI['MSSA2_MSIA'] = CoverageRate(MSSA2,MSIA)                

                        KPI['HSM_MSSA1'] = CoverageRate(HSM,MSSA1)
                        KPI['MSSA1_HSM'] = CoverageRate(MSSA1,HSM)

                        KPI['HSM_MSSA2'] = CoverageRate(HSM,MSSA2)
                        KPI['MSSA2_HSM'] = CoverageRate(MSSA2,HSM)  

                        KPI['HSM_MACO'] = ' \ ' # CoverageRate(HSM,MACO)
                        KPI['MACO_HSM'] =' \ ' #  CoverageRate(MACO,HSM)

                        KPI['HSM_GMPSO'] = CoverageRate(HSM,GMPSO)
                        KPI['GMPSO_HSM'] = CoverageRate(GMPSO,HSM)

                        KPI['HSM_GALCS'] = CoverageRate(HSM,GALCS)
                        KPI['GALCS_HSM'] = CoverageRate(GALCS,HSM)    

                        KPI['MSIA_HSM'] = CoverageRate(MSIA,HSM)
                        KPI['HSM_MSIA'] = CoverageRate(HSM,MSIA)               
        
                        KPI['MSIA_RunTime']= MSIA[len(MSIA)-1]['RunTime'] 
                        KPI['MACO_RunTime']= ' \ ' # MACO[len(MACO)-1]['RunTime'] 
                        KPI['GMPSO_RunTime']= GMPSO[len(GMPSO)-1]['RunTime']
                        KPI['GALCS_RunTime']= GALCS[len(GALCS)-1]['RunTime'] 
                        KPI['MSSA1_RunTime']= MSSA1[len(MSSA1)-1]['RunTime'] 
                        KPI['MSSA2_RunTime']= MSSA2[len(MSSA2)-1]['RunTime']                        
                        KPI['HSM_RunTime']= HSM[len(HSM)-1]['RunTime'] 

                        KPI['MSIA_AvgPBVMs']= MSIA[len(MSIA)-1]['AvgPBVMs']
                        KPI['MACO_AvgPBVMs']= ' \ ' # MACO[len(MACO)-1]['AvgPBVMs']
                        KPI['GMPSO_AvgPBVMs']= GMPSO[len(GMPSO)-1]['AvgPBVMs']
                        KPI['GALCS_AvgPBVMs']= GALCS[len(GALCS)-1]['AvgPBVMs']
                        KPI['MSSA1_AvgPBVMs']= MSSA1[len(MSSA1)-1]['AvgPBVMs']
                        KPI['MSSA2_AvgPBVMs']= MSSA2[len(MSSA2)-1]['AvgPBVMs']                        
                        KPI['HSM_AvgPBVMs']= HSM[len(HSM)-1]['AvgPBVMs']                        
        n += 1
        sheet[n, 0].value =  Labels[n_]
        for i in range(len(KPI_Name)):
            sheet[n, i+1].value= KPI[KPI_Name[i]]      
                
        ''' 优值标注'''      
        AlgorithmNumbers = 7
        '''max'''
        HV_List = [sheet[n, i+1].value for i in range(AlgorithmNumbers)]
        HV_List[0] = -inf if HV_List[0]== ' \ ' else  HV_List[0]
        for i in range(AlgorithmNumbers):
            if sheet[n, i+1].value == max(HV_List):
                sheet[n, i+1].api.Font.Color = 0x0000ff #red
                sheet[n, i+1].api.Font.Bold = True
                sheet[n, i+1].api.Font.Italic = True       
        
        
        CR_Num = (AlgorithmNumbers-2)*2+1
        start_num = KPI_Name.index('MSIA_MACO')
        for each in range(CR_Num):
            if sheet[n, start_num+1+each*2].value > sheet[n, start_num+1+1+each*2].value:
                sheet[n, start_num+1+each*2].api.Font.Color = 0x0000ff
                sheet[n, start_num+1+each*2].api.Font.Bold = True
                sheet[n, start_num+1+each*2].api.Font.Italic = True            
            elif sheet[n, start_num+1+each*2].value < sheet[n, start_num+1+1+each*2].value:
                sheet[n, start_num+1+1+each*2].api.Font.Color = 0x0000ff
                sheet[n, start_num+1+1+each*2].api.Font.Bold = True
                sheet[n, start_num+1+1+each*2].api.Font.Italic = True
        '''min'''
        minNum = 3
        start_num = KPI_Name.index('MACO_RunTime')
        for each in [0]:# range(minNum) ## 运行时间 第二小的加粗
            HV_List = [sheet[n, start_num+1+i+each*AlgorithmNumbers].value for i in range(AlgorithmNumbers)]
            HV_List[0] = inf if HV_List[0]== ' \ ' else  HV_List[0]
            min2 = sorted(HV_List)[1]
            for i in range(AlgorithmNumbers):
                if HV_List[i] <= min2:
                    sheet[n, start_num+1+i+each*AlgorithmNumbers].api.Font.Color = 0x0000ff #red
                    sheet[n, start_num+1+i+each*AlgorithmNumbers].api.Font.Bold = True  
                    sheet[n, start_num+1+i+each*AlgorithmNumbers].api.Font.Italic = True              
        for each in range(1,minNum):
            HV_List = [sheet[n, start_num+1+i+each*AlgorithmNumbers].value for i in range(AlgorithmNumbers)]
            HV_List[0] = inf if HV_List[0]== ' \ ' else  HV_List[0]
            for i in range(AlgorithmNumbers):
                if HV_List[i] == min(HV_List):
                    sheet[n, start_num+1+i+each*AlgorithmNumbers].api.Font.Color = 0x0000ff #red
                    sheet[n, start_num+1+i+each*AlgorithmNumbers].api.Font.Bold = True  
                    sheet[n, start_num+1+i+each*AlgorithmNumbers].api.Font.Italic = True            





    book.save(r'D:\OneDrive - kust.edu.cn\Ph.D\Procedure\Three\Results\SuccessfulRate\test-Revise.xlsx')
    book.close()
    app.quit()
k = 1