Returning an additional value that is not part of the fitness function or objective for all population evaluation in GA

Asked by Yaser Khojah on 22 Aug 2019

Latest activity Commented on by Yaser Khojah on 23 Aug 2019

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Can anyone please help by showing the code how can I return an additional value (Sigma) that is not part of my fitness function (Mean). I know how to return all the populations, scores but I'm not sure how to return this additional value (sigma). I see many have asked this question but no one showed in a code rather than just directing us to use the nested functions. Here is my code:

% the function to be optimized
 [objective]   = Optimization_Function(x,Pi,Pa,LogG,d,lifetime,Demand,BasePrice,HighPrice,...
  LowPrice,dis_rate_lamda,Geo,Wells_cost,Wells_rate,DStage,Operating_Fields,row,Amt,FOPEX,VOPEX)
.
.
.
.
expectedNPV  = mean(NPV_C);
sigma        = std(NPV_C); %%%%% I need this value for every population evaluation 
NewObjective = (expectedNPV)
objective    = - NewObjective; 

% my optimization code 
clear gaoutfunction
options          = optimoptions('ga','OutputFcn',@gaoutfunction,'UseParallel',true);
startTime        = tic;
fun              = @(x)Optimization_Function(x,Pi,Pa,LogNormal_G,d_cline,lifetime,Demand,BasePrice,...
    HighPrice,LowPrice,dis_rate_lamda,Geo,Wells_cost,Wells_rate,DStage,Operating_Fields,row,Amt,FOPEX,VOPEX);  
[xGA,fval]       = ga(fun,nvars,[],[],[],[],lowbond,upbond,[],[],options);
time_ga_parallel = toc(startTime);
record                = gaoutfunction();
gapopulationhistory   = vertcat(record.Population);
gabesthistory         = vertcat(record.Best);
gascorehistory        = vertcat(record.Score);
Results               = [gapopulationhistory gascorehistory];

% my output funciton which includes populations, scores but does not include sigma :(
function [state,options,optchanged] = gaoutfunction(options,state,flag)
    persistent state_record 
    if isempty(state_record)
      state_record = struct('Population', {}, 'Best', {}, 'Score', {});
    end
    if nargin == 0
      state = state_record;
      options = [];
      optchanged = [];
    else
      state_record(end+1) = struct('Population', state.Population, 'Best', state.Best', 'Score', state.Score);
      optchanged = false;
    end
end 

Can anyone please show me how to return sigma as I’m returning all the populations and their scores. Please note the score here is only the objective which is the mean. The sigma value is not part of the optimization but I need it to save the time rather than running the model again to evaluate it. Please help.

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Answer by Matt J on 22 Aug 2019

Edited by Matt J on 22 Aug 2019

Accepted Answer

function run_optimization
    allSigmas=[];
    [xGA,fval]   = ga(fun,nvars,[],[],[],[],lowbond,upbond,[],[],options);
    
    
     % the function to be optimized
    function  [objective]   = Optimization_Function(x,Pi,Pa,...)        .
                     ....
                         
           objective    = - NewObjective; 
          allSigmas(end+1)      = std(NPV_C); %%%%% I need this value for every population evaluation 
        
    end
end

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Yaser Khojah on 22 Aug 2019

workspace.JPG

I put it in your format but I get only one value which is the initial value instead all of entire population (15200) as attached. Please check the first and last line where I reflected what you recommended as below. Thanks for your help!

function run_optimization
allSigmas=[];
%==========================================================================
%==========================================================================
%--------------------------------------------------------------------------
% *************NUMBER OF YEARS AND DISCOUNTE RATE WERE CAHANGE ************
%==========================================================================
%==========================================================================
%==========================================================================
% 
array_index        = '9'; 
% array_index        = getenv('PBS_ARRAY_INDEX');
iii                = str2num(array_index);
global drwaing 
Field       = 1;
SD_Range    = [0:4, 5:5:30];
% SD_Range    = 5;
array_index = size(SD_Range,2);
filename    = 'Fields_Input_Amt_OPT.json';
% for iii = 1:array_index
fprintf('%i\n', iii)
% the sampling was fixed here? please check
drwaing = 0; 
% filename = 'Fields_Input_Amt.json';  
[Pi,Pa,G,d,DStage,Wells_rate,Wells_cost,Geo,BasePrice,LowPrice,HighPrice,lifetime,Demand,dis_rate_lamda,DOFF,SOFF,On,Operating_Fields,Amt,FOPEX,VOPEX]= New_readinput_OPEX(filename);
% create the log normal for everyone
CE         = 0;
row        = 10; 
G_MC       = G/10^6; % convert G in place from MMSCF to TSCF
Mean       = G_MC; 
std_       = Mean .* CE; 
Variance   = std_.^2; 
mu         = log((Mean.^2)./sqrt(Variance+Mean.^2));
sigma      = sqrt(log(Variance./(Mean.^2)+1));
GIP        = zeros(row,length(G_MC));
        
for index = 1:length(G_MC)
    R   = lognrnd(mu(index),sigma(index),[row,1]);
    GIP(:,index) = R; 
    
end 
% create log-normal for 1 only
m            = G_MC(Field); 
sd           = SD_Range(iii);
v            = sd^2;
mu           = log((m^2)/sqrt(v+m^2));
sigma        = sqrt(log(v/(m^2)+1));
GIP(:,Field) = lognrnd(mu,sigma,[row,1]);
       
% put the unit back to MMSCFD
LogNormal_G      = GIP * 10^6;
rmax             = G .*d .* ones(size(LogNormal_G));
d_cline          = rmax ./ LogNormal_G; 
fnm = sprintf('GIP_%d.mat',SD_Range(iii));
save(fnm,'LogNormal_G');
fnm = sprintf('D_%d.mat',SD_Range(iii));
save(fnm,'d_cline');
% Update the pressure 
% pi_updated =  update_pressure(Pi, G, LogNormal_G, row);
% Run Optimizer
ub           = [3500, 2000, 1000, 3500, 2500,1000, 700, 2500];
lb           = [2000,  700,  400, 1000, 1000, 300, 300,  800];
nvars         = Operating_Fields*2;
MaxProduction = ub *365; 
MinProduction = lb *365;
t_bound       = lifetime;
upbond        = [MaxProduction  t_bound* ones(1,Operating_Fields)]; 
% lowbond       = [MinProduction  zeros(1,Operating_Fields)]; 
lowbond       = [MinProduction  -1 * ones(1,Operating_Fields)]; 
clear gaoutfunction
options          = optimoptions('ga','OutputFcn',{@gaoutfunction},'UseParallel',true);
startTime        = tic;
fun              = @(x)Optimization_Function(x,Pi,Pa,LogNormal_G,d_cline,lifetime,Demand,BasePrice,...
    HighPrice,LowPrice,dis_rate_lamda,Geo,Wells_cost,Wells_rate,DStage,Operating_Fields,row,Amt,FOPEX,VOPEX);  
[xGA,fval]       = ga(fun,nvars,[],[],[],[],lowbond,upbond,[],[],options);
time_ga_parallel = toc(startTime);
record                = gaoutfunction();
gapopulationhistory   = vertcat(record.Population);
gabesthistory         = vertcat(record.Best);
gascorehistory        = vertcat(record.Score);
Results               = [gapopulationhistory gascorehistory];
fnm = sprintf('Optimization_Case_%d.mat',SD_Range(iii));
save(fnm);
fnm = sprintf('Results_%d.mat',SD_Range(iii));
save(fnm,'Results');
function [objective]   = Optimization_Function(x,Pi,Pa,LogG,d,lifetime,Demand,BasePrice,HighPrice,...
  LowPrice,dis_rate_lamda,Geo,Wells_cost,Wells_rate,DStage,Operating_Fields,row,Amt,FOPEX,VOPEX)
r_s          = x(1:Operating_Fields); 
t_0          = floor(x(Operating_Fields+1:end)); 
column       = size(r_s,2);
Supply       = zeros(lifetime+1,row);  
Revenue      = zeros(row,1);  
OPEX         = zeros(row,1);  
CapitalRisk  = zeros(row,1);
% F_off        = zeros(row,1);
[t_SS , t_A] = time_resevoir_Stochastic_Function(Pi,Pa,LogG,r_s,t_0,d,row,column);
% CAPEX = Total discoutned
% Dis_CAPEX = Discounted per field 
% CAPEX_Field = Captial per field without discounting
[CAPEX ,Dis_CAPEX, CAPEX_Field]  = OPT_CAPEX(r_s,t_0,Geo,Wells_cost,Wells_rate,dis_rate_lamda,DStage,row,column,Amt); 
for i = 1:row
[Supply_Cum, Field_No_Prod,Production_Profile_Cum,TimeSteps] = OPT_Supply(lifetime,t_0,t_SS(i,:),t_A(i,:),r_s,d);%^^^^^^
 Supply(:,i)            = Supply_Cum; 
[~,~,~,Reveune_Total,~] = New_Penalty_Average_Price_Function(Demand,Supply_Cum,BasePrice, HighPrice,LowPrice,dis_rate_lamda,lifetime);
Revenue(i)              = Reveune_Total; 
% The OPEX_ros is the total discounted rate 
[OPEX_row,~]   = OPT_OPEX(Production_Profile_Cum, CAPEX_Field(1,:),VOPEX, FOPEX,TimeSteps,lifetime,dis_rate_lamda);  %^^^^^^
OPEX(i)        = OPEX_row ; %^^^^^^
CAPEX_risk     = Dis_CAPEX(i,(Field_No_Prod));
CapitalRisk(i) = sum(CAPEX_risk,2);
%     if Field_No_Prod>0
%     F_off(i,1)     = Field_No_Prod; 
%     end 
end 
NPV_C        = (Revenue - CAPEX - OPEX); %^^^^^^
expectedNPV  = mean(NPV_C);
sigma        = std(NPV_C);
NewObjective = (expectedNPV + 0*sigma); 
objective    = - NewObjective;
allSigmas(end+1) = std(NPV_C); 
end 
end

allisgmas is only one value while my rest of populations are 15200 as attaced:

Yaser Khojah on 23 Aug 2019

Dear Matt, thanks for sharing this. I have tried and it is working. I never used parfor before. Do you think 'UseVectorized' is faster than using the parallel?

Matt J on 23 Aug 2019

UseVectorized can be faster than parfor-parallelization when the fitness of all population members can be computed all at once without a for-loop. I assumed that that was not the case for you.

Yaser Khojah on 23 Aug 2019

I agree, thanks :)

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