Get parallel simulations in objective function of optimization Tool (GA,PSO)

Question

Marcus Heßeling 2021-2-12

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/743512-get-parallel-simulations-in-objective-function-of-optimization-tool-ga-pso

评论： Irfan Khan 2022-2-26

采纳的回答： Abdolkarim Mohammadi

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Hey Guys

i have an optimization problem using the constrainted PSO:

https://de.mathworks.com/matlabcentral/fileexchange/25986-constrained-particle-swarm-optimization

The syntax and general working of the constrainted PSO is the same as GA.

In my objective function, i have to generate a simOut with data from a simulink simulation. One calculation needs about 7 hours of time, since the simulation needs about 3 seconds.To increase the algorithm speed, i want the simulations to run parallel. But it doesnt seem to work if i use "parpool('local')". So far ich changed the settings to "UseVectorized" so have the whole population in one objective function. My code looks like this:

parpool('local')
    options = psooptimset('Display','iter','OutputFcns',@myoutputFunc,'UseParallel',true,...
                          'Generations',200,'PopulationSize',40,'Vectorized','on');
    
    [xopt,fval,exitflag,output,population,scores] = pso(@objective,9,[],[],[],[],lb,ub,@constraint,options);
    function obj = objective(x)     
    % WANT TO: check the number of workers , in my case 6 
            % WANT TO: set parallel simulations equal to work number
           for p=1:length(x(:,1)) %WANT TO: set parallel working parfor loop to execute the parallel simulations
     
           SimuGeometrics = calculateGeometrics(x(p,:));            %SimuGeometrics is a structure of lengths and angle of different body that are used for simulink
           %start simulation
           simOut = sim('Mechanism.slx','CaptureErrors','on','SrcWorkspace','current');
            %Result
            pos1 = [simOut.xx.data, simOut.yy.data, simOut.zz.data];
            
            obj(p,1) = calculateDeviation(pos1);
            
            disp(['x: ',num2str(x(p,:)) ,' obj: ',num2str(obj(p,1))]);
           end
    end
 

I know that there are some functions like parfor or parsim but i dont know how to include them into my Code.

Of cause, my WANTO TO's are only ideas how i think it can work, if for example setting a parfor loop is nonsense, you can suggest another solution

Edit:

@Abdolkarim Mohammadi-Balani

i saw your Answer from :https://de.mathworks.com/matlabcentral/answers/595402-how-to-use-the-parallel-computing-toolbox-of-matlab-to-optimize-the-parameters-of-a-simulink-model-w#answer_496378

it seems like you described a possible solution for my problem. I will try out what you described today and will share my solution if i succeed, but it would help me alot if u could share your expertise on this.

I hope somebody can help me with this problem.

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Answer 1

Abdolkarim Mohammadi 2021-2-12

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Hi Marcus.

I haven't used that File Exchange submission for PSO, but the general workflow in such situations is to:

Pass the whole population to the objective function. This is done by setting the UseVectorized option to true.
When the whole population is received by the objective function, it must create a SimulationInput object. You can learn about creating it here. Your SimulationInput object is an array, where each element represents a chromosome in the population.
Then you pass the single SimulationInput object to parsim().
Next, simulations are run and the SimulationOutput object is returned.
Finally, you calculate the objective function values from the SimulationOutput object and return them to the solver.

The objective funciton's code might look something like this:

function f = ObjFun(x)
% get population size 
PopulationSize = size(x, 1);
% create the SimluationInput object 
for i1 = 1:PopulationSize 
    SimIn(i1) = Simulink.SimulationInput('MyModel');
    SimIn(i1) = SimIn(i1).setVariable('MyVar1', x(i1,1))
    SimIn(i1) = SimIn(i1).setVariable('MyVar2', x(i1,2))
    % do it for all of the variables
end
% run the simulations and get the SimulationOutput object 
SimOut = parsim(SimIn); 
% initialize the arrays containing the objective function values 
f = zeros (PopulationSize, 1);
% fill f with objective function values 
for i1 = 1:PopulationSize 
    f(i1) = SimOut(i1).MyOutput; 
end
end

Read about setVariable function here.

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Marcus Heßeling 2021-2-14

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Hey @Abdolkarim Mohammadi-Balani,

thank you very much for your help, it worked perfectly. In your other post you were talking about speeding up the simulation by using RapidAccelerator or FastRestart. RA doesnt work, since it doesnt provide any output data, but the Accelerator does work. The FastRestart doesnt work, and im curious:

As far as i understand it, in the objective i build 40 SimIn Models for every particle. Every SimIn Model is only performed once, so the FastRestart wouldnt be of any use in this case right ? Since i only change the variables in my Models, a FastRestart could be possible? is there a way to implement it into my Code, for example buy only make SimIn Models equal to my number of workers and then reuse the SimIn until i reach the 40 particles ? The problem i see is in the unequal numbers of iterations (40 / 6 workers)

For everyone else who want to know my made code, its provided in the following. Its very similar to Abdolkarims Version ( Particle in PSO is the same as Chromosomes in GA)

function obj = objective(x)
 
PopulationSize = size(x, 1);
startAngle=1;
 
    % create the SimluationInput object for every particle
    for i1 = 1:PopulationSize 
        SimuGeometrics = calculateGeometrics(x(i1,:)); 
        % Set Model
        SimIn(i1) = Simulink.SimulationInput('Mechanism');
 
        %Set Model Parameters
 
        %SimIn(i1) = SimIn(i1).setModelParameter('SimulationMode','accelerator');
        %SimIn(i1) = SimIn(i1).setModelParameter('UseFastRestart', 'on');
 
        %Set Variables
        SimIn(i1) = SimIn(i1).setVariable('SimuGeometrics', SimuGeometrics);
        SimIn(i1) = SimIn(i1).setVariable('startAngle', startAngle);
    end
    
    %start simulation
     SimOut = parsim(SimIn);
     
     
    %load wanted Model 
    [perfectModel] = perfectModelLoading;
    
    
    %hand over the objectives for every particle and display results     
    obj =zeros(PopulationSize,1);  
    
    for i2 = 1:PopulationSize
            
        [disVec]= calculateDeviation(perfectModel,SimOut(i2));    
 
        obj(i2,1) = mean(disVec) + max(disVec);
    
    disp(['Particle: ',num2str(i2),' x: ',num2str(x(i2,:)) ,' obj: ',num2str(obj(i2,1))]);
    end
 
end

Abdolkarim Mohammadi 2021-2-27

在 MATLAB Online 中打开

I reviewed your code and I found out that you didn't use the proper methodology for multiple simulation runs. You wrote:

for i0 = ...
    simOut = sim('Opt_9Facets_Model_Parallel.slx','CaptureErrors','on','SrcWorkspace','current',...
                        'FastRestart','on');
end

Which is the worst way to run multiple simulations. I told you that you should use SimulationInput objects, not calling sim() using model name. Using sim() with model name as the first input and fast restart enabled, prevents Simulink engine from updating simulation parameters in future runs. That's why you are getting the same objective function value in each run. The first run with fast restart LOCKS the model, and future runs do not read parameters from workspace (either base workspace or model workspace).

Irfan Khan 2022-2-26

@Abdolkarim Mohammadi I used the objective function code and simulation is very fast now. But since i am new matlab user i made some mistakes. I fitness value graphs is abnormal. and the values in varriable solution2 are not giving stabilized output.

The background is i am tuning a PID controller in simulink using the genetic algorithim optimization tool. My cost function is "to workspace" (ITAE). In serial simulation the old objective function was working fine but in parallel simulation objective function, the output is not correct becuase i am not sure what to right in this line

cost(i2,1) = SimOut.ITAE(end);

Parallel Simulation objective function which is running but not tunning

function cost= tunning(k)

% get population size

PopulationSize = size(k, 1);

% create the SimluationInput object

for i1 = 1:PopulationSize

SimIn(i1) = Simulink.SimulationInput('test');

SimIn(i1) = SimIn(i1).setVariable('k(1)', k(i1,1));

SimIn(i1) = SimIn(i1).setVariable('k(2)', k(i1,2));

SimIn(i1) = SimIn(i1).setVariable('k(3)', k(i1,3));

% do it for all of the variables

end

% run the simulations and get the SimulationOutput object

SimOut = sim("test.slx",'CaptureErrors','on','SrcWorkspace','current');

%cost= ITAE;

% initialize the arrays containing the objective function values

cost= zeros (PopulationSize, 1);

% fill cost with objective function values

for i2 = 1:PopulationSize

cost(i2,1) = SimOut.ITAE(end);

end

Serial Objective Function which is slow but give right result

function cost = tunning_serial(k)

assignin('base','k',k);

sim('test.slx');

cost= ITAE(length(ITAE));

end

My Simulink Model using PID controller

Live Editor Optimization using Genetic Algorithim

% Set nondefault solver options

options2 = optimoptions("ga","UseVectorized",true,"CreationFcn",...

"gacreationuniform","SelectionFcn","selectiontournament","MutationFcn",...

"mutationadaptfeasible","Display","iter","PlotFcn","gaplotbestf");

% Solve

[solution2,objectiveValue] = ga(@tunning,nVariables,[],[],[],[],...

zeros(nVariables,1),repmat(5,nVariables,1),[],[],options2);

Abnormal Fitnessvalue graphs

I guess this line Simout.ITAE(end) is the problem.

I have attached my objective function files

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Get parallel simulations in objective function of optimization Tool (GA,PSO)

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