1. |
|
|
2. |
|
|
3. |
- Hartikainen, Markus, 1981-, et al.
(författare)
-
Constructing a Pareto front approximation for decision making
- 2011
-
Ingår i: Mathematical Methods of Operations Research. - : Springer Berlin/Heidelberg. - 1432-2994 .- 1432-5217. ; 73:2, s. 209-234
-
Tidskriftsartikel (refereegranskat)abstract
- An approach to constructing a Pareto front approximation to computationally expensive multiobjective optimization problems is developed. The approximation is constructed as a sub-complex of a Delaunay triangulation of a finite set of Pareto optimal outcomes to the problem. The approach is based on the concept of inherent nondominance. Rules for checking the inherent nondominance of complexes are developed and applying the rules is demonstrated with examples. The quality of the approximation is quantified with error estimates. Due to its properties, the Pareto front approximation works as a surrogate to the original problem for decision making with interactive methods.
|
|
4. |
|
|
5. |
|
|
6. |
|
|
7. |
- Hartikainen, Markus, 1981-, et al.
(författare)
-
PAINT : Pareto front interpolation for nonlinear multiobjective optimization
- 2012
-
Ingår i: Computational optimization and applications. - : Springer. - 0926-6003 .- 1573-2894. ; 52:3, s. 845-867
-
Tidskriftsartikel (refereegranskat)abstract
- A method called PAINT is introduced for computationally expensive multiobjective optimization problems. The method interpolates between a given set of Pareto optimal outcomes. The interpolation provided by the PAINT method implies a mixed integer linear surrogate problem for the original problem which can be optimized with any interactive method to make decisions concerning the original problem. When the scalarizations of the interactive method used do not introduce nonlinearity to the problem (which is true e.g., for the synchronous NIMBUS method), the scalarizations of the surrogate problem can be optimized with available mixed integer linear solvers. Thus, the use of the interactive method is fast with the surrogate problem even though the problem is computationally expensive. Numerical examples of applying the PAINT method for interpolation are included.
|
|