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Results tagged with multi-objective-optimization
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Multi-objective optimization is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Involve two or more optimization goals that are conflicting, meaning that improvement to one objective comes at the expense of another objective. The two methods for perform a multi-objetive-optimization are Pareto and scalarization.
5
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Do you know production deployments of multi-objective optimization?
Not a direct answer, but I think that "interactive multiobjective optimization" has shown that it can overcome the obstacle of expontially man Pareto optimal solutions. I have attended talks and semin …
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2
votes
Accepted
Which exact method can find all pareto-optimal solutions of a multi-objective optimization p...
Whether a particular method is capable of generating all efficient solutions is often a question of what structure the MOOP (multi objective optimisation problem) has.
In addition, you ask for methods …
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1
vote
Do you have to normalize objectives when using the weighted sum approch?
It depends on what you want to achieve, but I would like to argue, contrary to the answer by @Merve Özer, that you do not need to (explicitly) normalize the objectives.
If you have, two objective func …
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1
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Problem of scaling or normalizing in multiobjective optimization problem when one objective ...
You write in a comment that "you want to scale $f_1$ and $f_2$ so that they are as big as the others". If I understand you correctly, this may be impossible if you do not know the range of values for …
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5
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Multi-objective function normalization
Yes. There are plenty of other approaches to handle multiple objectives. First of all, you need to figure out, what you consider an optimal solution (set) to your multi objective optimization problem. …
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3
votes
Accepted
Could non-supported efficient solutions in multi-objective optimization problem be an optima...
No. If you have strictly positive weights you will get a supported efficient solution by solving the weighted sum scalarization, regardless of whether the weights sum to one or not. Requiring the weig …
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1
vote
Accepted
How to get hypervolume calculation for Pareto Front in python?
It seems the Pymoo package has the machinery to compute the hypervolume. From the documentation, in the "Performance indicator" section, they describe several performance indicators (generational dist …
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4
votes
weight choice in multi-objective weighted sum
Do you think the weights I choose are logical?
No, I don't think they are logical, but that is because you have chosen them randomly (according to your description). Hence, there is not really any log …
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4
votes
Accepted
Does the weighted sum approach find all pareto-optimal solutions in MILP
No. You cannot be sure to find all Pareto optimal solutions to a MILP using the weighted sum approach. You are not even guaranteed to find all non-dominated outcomes.
You are only guaranteed to be abl …
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1
vote
Accepted
Multi objective optimization is giving wrong results
It seems right to me. If you want to maximise priority, you would have to sacrifice cost-wise. Hence, the costs goes up. If you want to minimise costs, you cannot select all the high-priority nodes, h …
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7
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Benchmark problems for combinatorial multi-objective optimization
There is also the MOrepo maintained by Lars Relund Nielsen. MOrepo describe itself as:
This repository is a response to the needs of researchers from the MCDM society to access multi-objective (MO) o …
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0
votes
Is there a name for this variation of the generalized assignment problem?
This sounds like a lexicographic optimization problem with two objectives: "maximize the number of tasks satisfied" is the high-priority objective while "minimize the cost" is the low priority objecti …
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How to find the feasible solution when the value of the objective function is known?
I don't see any easy way to obtain a feasible pre-image of a specific (approximate) non-dominated outcome vector - especially as there might not exist one.
One way to proceed is to add slack and surpl …
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2
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Branch and bound method for solving non-convex integer non-linear multi-objective optimizati...
Julia Niebling and Gabriele Eichfelder recently published a paper on solving nonconvex multiobjective optimization problems using branch and bound. You can find the paper through the following link ht …
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