Genetic Algorithm + Parallel Computing + Global variables
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Hello to all,
I have been able to run a GA in parallel (10 workers in total). Now I want to store in a kind of "result matrix" what happens on each worker. For example the simulated individual, the CPU time, the cost and the number of iterations. I tried defining global variables in the main script, where I just call the ga(). Then in the objective function I call the global variables and assign new values. Unfortunately, I only get one row in my "result matrix".
I also tried parfevalOnAll and in the called function I load and assign the values. But this does not work at all.
Does anyone have any idea how to handle this?
Thanks a lot already!
回答（2 个）
If you're using UseVectorized, it should be relatively easy. Within your parfor loop, store the results of processing each population member to a struct array and store that to an externally scoped cell array ResultMatrix as below. Later you can aggregate the different ResultMatrix{i}(j) according to the worker label stored in ResultMatrix{i}(j).labindex.
function main
ResultMatrix={};
opts=optimoptions('ga','UseVectorized',true);
x=ga(@fitnessFunc,nvars,A,b,Aeq,beq,lb,ub,nonlcon,intcon,opts));
function out=fitnessFunc(X)
parfor i=1:size(X,1)
tic;
%process population members here
T=toc;
S(i).cpuTime=T;
S(i).popmember=X(i,:);
...
S(i).labindex=getCurrentTask().ID;
S(i).cost=cost;
end
ResultMatrix{end+1}=S;
out=vertcat(S.cost);
end
end
6 个评论
Jonas
2022-8-27
Matt J
2022-8-27
Because I have a very high simulation time, I think I can't use UseVectorized.
Not sure why that matters.
Jonas
2022-8-27
To be honest I don't really understnad what this is excatly doing.
You probably need to read about what UseVectorized does. Basically, it will pass all members of the current population to the objective function at once in a single input matrix X. When you do this, your objective function has the responsibility for computing the cost of all population members X(i,:) in the current generation. You can divide up this work with whatever parallel computing toolbox functions you wish, in the case of my example with parfor. The advantage of this is that your objective function becomes the workspace where the parallelization happens, so you have access to it and can save the results to external variables like ResultsMatrix.
in my case, the ga is calling the objective function in parallel. So I don't have a parfor loop in my function. Otherwise, your code would solve my problem.
Yes, but the whole point of this forum is that we can suggest you stop doing what you're doing and do something else that might better.
Jonas
2022-8-28
Walter Roberson
2022-8-28
global variables never transfer between parallel workers or workers and client.
Do you or anyone else have any other ideas for a workaround? It doesn't have to be the prettiest solution.
You might be able to accumulate the results to a .mat file, as opposed to a global variable. In your fitness function, you would have something like,
filename="outfile"+getCurrentTask().ID; %worker-dependent temp file
result_matrix=[load(filename).result_matrix, result_matrix];
save(filename,'result_matrix');
1 个评论
Matt J
2022-8-28
You could also do the same kind of thing in a custom Output Function to add things like the current iteration number to the temp files.
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