# fmincon is giving better results than ga for a nonlinear constrained optimization problem.

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Nayan Rawat on 12 Jul 2019
Edited: Matt J on 12 Jul 2019
I used fmincon and ga for the same nonlinear constrained optimization (7-300 variables) but fmincon is giving much better results (all ceq<10^-8) while ga is not able to satisfy equality constraints. Help please.
Also: fmincon results vary with initial guess, step tolerance and constraint tolerance. What step tolerance and constraint tolerance should i choose?

Matt J on 12 Jul 2019
What help is required? If fmincon responds better to the problem, why not just use that instead?
Nayan Rawat on 12 Jul 2019
Because fmincon result depends on initial guess. Changing the initial guess is changing the result while the ceq constraints are still very small(1e-8). This means that it may be a local minimum. I want to use other algorithms which give global minimum too (like ga, patternsearch etc).

Star Strider on 12 Jul 2019
The ga function produces its best results if you override the default population matrix with one of your own. I always use an options structure similar to:
PopSz = 500;
Parms = 6;
opts = optimoptions('ga', 'PopulationSize',PopSz, 'InitialPopulationMatrix',randi(1E+4,PopSz,Parms)*1E-3, 'MaxGenerations',2E3, 'PlotFcn',@gaplotbestf, 'PlotInterval',1);
where ‘PopSz’ is the size (dimension 1) of the population matrix, and ‘Parms’ (dimension 2) is the number of parameters to optimise. It takes a bit longer, however it almost always converges successfully, if a solution exists. I use randi to more efficiently control the range of the random matrix.

Matt J on 12 Jul 2019
Or/also:
Add one more of the solutions from fmincon to the initial population. Since those solutions satisfy the constraints, they may be good seeds for ga().
Nayan Rawat on 12 Jul 2019
Thakks for the suggestion. I tried it but GA is not satisfying ceq even after taking 10 times more run time.
Nayan Rawat on 12 Jul 2019
Thanks, will try.

R2019a

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