GA violates the linear inequality constraints

Hi. I have mixed integer non-linear optimization problem with two optimization variables, each of size K*M*N. For now I have tried the linear inequality constraitns. The result of opt. variable does not meet the constraints.

I have attached the complete code. Please highlight the issues.

Also, can ga solve this problem?

Thanking you in advance.

Answer 1

[Alan Weiss]

You have expressed your constraints as nonlinear inequality constraints, not as linear inequality constraints. If the constraints are truly linear (they don't look linear to me), then you might have better luck expressing them in the format of linear inequality constraints.

You have a relatively large number of binary variables, and a small population. I would not expect ga to perform well on this kind of problem. And I do not understand why you set EliteCount to 1.

It seems to me that you have 23 binary variables. That is only 8388608 possibilities. Perhaps exhaustive search would work.

Good luck,

Alan Weiss

MATLAB mathematical toolbox documentation

[Umber Saleem]

Thank you for the guidence. The issue is resolved since I have corrceted my constraitns.

The number of variables is large in my case. I have KxMxN binary variables and KxMxN other variables as my problem is MINLP. Would exhaustive search work for such kind of problem?

I am not very clear about the parameter values in ga. How oto determine optimal population size, generations and elitecount?

[Alan Weiss]

As far as options go, I suggest that you do not set values for parameters that you do not understand. You current settings are

options=gaoptimset('Generations',1000,'PopulationSize',100,'EliteCount',1,'TolFun',1e-8);

In particular, I suggest that you do not set EliteCount or Generations. I strongly suggest that you set PopulationSize to something much, much larger, perhaps 1e4 or even more. This setting will slow down your optimization a great deal, but you will have a much better chance of arriving at a good answer. To see what the various option settings do, see Genetic Algorithm Options.

Good luck,

Alan Weiss

MATLAB mathematical toolbox documentation

[Umber Saleem]

Thank you sir.