Please use this identifier to cite or link to this item:
|Title:||Continuous relaxation of MINLP problems by penalty functions: a practical comparison|
|Author(s):||Costa, M. Fernanda P.|
Rocha, Ana Maria A. C.
Fernandes, Edite Manuela da G. P.
|Journal:||Lecture Notes in Computer Science|
|Citation:||Costa M.F.P., Rocha A.M.A.C., Fernandes E.M.G.P. (2017) Continuous Relaxation of MINLP Problems by Penalty Functions: A Practical Comparison. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science, vol 10406. Springer, Cham|
|Abstract(s):||A practical comparison of penalty functions for globally solving mixed-integer nonlinear programming (MINLP) problems is presented. The penalty approach relies on the continuous relaxation of the MINLP problem by adding a specific penalty term to the objective function. A new penalty algorithm that addresses simultaneously the reduction of the error tolerances for optimality and feasibility, as well as the reduction of the penalty parameter, is designed. Several penalty terms are tested and different penalty parameter update schemes are analyzed. The continuous nonlinear optimization problem is solved by the deterministic DIRECT optimizer. The numerical experiments show that the quality of the produced solutions are satisfactory and that the selected penalties have different performances in terms of efficiency and robustness.|
|Appears in Collections:|
Files in This Item:
|MINLP-Penalties-DIRECT_MFPCosta.pdf||200,15 kB||Adobe PDF||View/Open|