Using MATLAB to control Topology Optimization module in COMSOL
Posted 02.07.2026, 05:18 GMT-4 LiveLink for MATLAB, Studies & Solvers, Modeling Workflow Version 6.4 0 Replies
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**Help needed in implementing MATLAB script to maintain control over COMSOL Topology Optimization iterations **
Hello everyone,
As the title suggests, I want to use MATLAB Livelink with COMSOL to implement Topology Optimization. The concept is simple. For beta parameter between 1 and 1024, I want to control the number of iterations with three methods: 1 - Max iterations depending on beta value (this works) 2 - Ending iterations early based on some arbitary convergence criteria (this also works) 3- Reusing solution from previous beta as the initial solution for the next beta ( This does not work).
I was wondering if anyone has any idea on how this can be implemented without using the parametric sweep feature of COMSOL as that would disable controls 1 and 2 mentioned above.
Here is my script:
clc;clear %% Topology Optimization for a Micromixer with multiple studies
import com.comsol.model.* import com.comsol.model.util.*
%% Progress log logFile = 'D:\Users\pq6034\Desktop\HIWI\progress.log'; fid = fopen(logFile,'w'); fclose(fid); ModelUtil.showProgress(logFile); ModelUtil.showProgress(true);
%% Define model to study model = mphopen('NSCD_OPEN_3.mph');
%% Initial Variables nmax = 100; chunksize = 10; betas = 0:1:10; nupd = 30;
%% Output folder saveDir = 'Results_Micromixer_30.06.26'; if ~exist(saveDir,'dir') mkdir(saveDir); end
%% Automatically build study -> solver map studySolverMap = containers.Map; solTags = model.sol.tags(); fprintf('\nStudy-to-solver mapping:\n'); fprintf('--------------------------------------------\n');
for i = 1:length(solTags) solTag = char(solTags(i)); studyTag = char(model.sol(solTag).feature('st1').getString('study')); studyLabel = char(model.study(studyTag).label()); studySolverMap(studyTag) = solTag;
fprintf('%-5s (%-20s) --> %s\n', ...
studyTag, studyLabel, solTag);
end
fprintf('--------------------------------------------\n\n');
%% Studies to run studies = {'std','std1','std2','std3','std4','std5'}; %studies = {'std2'};
for s = 1:length(studies)
studyTag = studies{s};
fprintf('\n====================================\n');
fprintf('Running study %s\n',studyTag);
fprintf('====================================\n');
%% Solver tag
solTag = studySolverMap(studyTag);
fprintf('Using solver %s\n',solTag);
info = mphxmeshinfo(model,'soltag',solTag);
idx_theta_c = find(info.dofs.nameinds == 2);
sv_idx = info.dofs.solvectorinds(idx_theta_c);
theta_saved = [];
%% Beta loop
for k = 1:length(betas)
beta = 2^betas(k);
fprintf('\n====================================\n');
fprintf('Beta = %g\n',beta);
fprintf('====================================\n');
model.param.set('betap',num2str(beta));
%% Maximum iterations for this beta
if beta < 1024
maxIter = nupd;
else
maxIter = nmax;
end
%% First optimization chunk
model.sol(solTag).feature('o1').set('mmamaxiteractive',true);
model.sol(solTag).feature('o1').set('mmamaxiter',chunksize);
fprintf('Running first %d iterations...\n',chunksize);
model.sol(solTag).runFrom('o1');
counter = 10;
%% Call function to evaluate change in objective
[converged,totalChange] = monitorObjective(logFile);
%% Continue optimization
while ~converged && counter < maxIter
counter = counter + 10;
model.sol(solTag).feature('o1').set('mmamaxiter',chunksize);
fprintf('Continuing to %d iterations...\n',counter);
model.sol(solTag).continueRun();
[converged,totalChange] = monitorObjective(logFile);
end
fprintf('Finished beta = %g\n',beta);
%% Checkpoint save for beta
mphsave(model,fullfile(saveDir,...
sprintf('%s_beta_%g.mph',studyTag,beta)));
end %% End of Beta loop
%% Save study
mphsave(model,fullfile(saveDir,...
sprintf('%s_finalcompiled.mph',studyTag)));
end %% End of study
%% Final save mphsave(model,fullfile(saveDir,'Runx_final.mph')); disp('All studies completed.');
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