fmincon in matlab for multi objective

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Hello I am a question please
can I use fmincon function for solving multi objective linear programming in Matlab?
I want to create two m files, one of them is as objective function and the other is as constraint function and then I want to use fmincon to solve them.
can I do that? Is it true?

Accepted Answer

Dinghuan Zhu
Dinghuan Zhu on 22 Jul 2014
There are several approaches to tackle multi-objective optimization problems. You may refer to the following link for detailed descriptions:
If you are using the first approach "Goal attainment" mentioned in that document, you can definitely use the function "fmincon" to solve the problem as if you are solving any single-objective optimization problems.

More Answers (2)

Mohit  Verma
Mohit Verma on 15 Jan 2018
Edited: Mohit Verma on 15 Jan 2018
Hi Dinghuan,
Could you provide sample code that would show how we could do this? How would we do this to incorporate the pareto front, as in this example here:
Walter Roberson
Walter Roberson on 15 Jan 2018
"Can't the pareto front be generated by defining an effective objective function with multiple objectives defined using weights?"
No. Pareto front calculations depend upon the fitness of each objective being available directly. Pareto fronts are not defined by weighted values: they are defined by not making any of the goals larger by changing the locations. A change than makes one goal a million lower but raises another goal by one one-millionth still violates pareto front, but would be treated as an improvement by a weighted system.

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PkStefan on 21 Aug 2020
This is an old question but up to date for me. So can't you just normalize your objectives to obtain a single objective function and apply fmincon? For Normalization I'd use Min-Max Feature scaling. You could than even add weighing factors:
fun = weight1*norm(obj1)+Weight2*norm(obj2)
However you would need to have the minimum and maximum values of your objectives. Is this a legitimate way for applying fmincon?
Best regards, Stefan
Walter Roberson
Walter Roberson on 28 Sep 2020
In the case of continuous functions defined over bounds, there is the prospect of taking the derivative and solving for zero and substituting those locations into the second derivative to determine whether they are min or max or saddle, and thus hope to establish the range of values that way (remember to evaluate right at the boundaries as well.)
But if you can actually do that for every function and get out numeric locations that are not parameterized by other variables, then you have the full set of locations needed to be examined to find the pareto front, without having to do any further searching.
If you get out solutions that are parameterized by other variables then sometimes you can decide whether you have a max or min (e.g. powers are all even and coefficients are all the same sign. More often you would substitute the parameterized location intothe next equations, work on analyzing the results, do more substitutionss, and so on.
But with nonlinear equations a lot of the time you simply cannot solve the derivative for zero systematically...

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