Nonlinear biobjective optimization: improvements to interval branch & bound algorithms

Interval based solvers are commonly used for solving single-objective nonlinear optimization problems. Their reliability and increasing performance make them useful when proofs of infeasibility and/or certification of solutions are a must. On the other hand, there exist only a few approaches dealing...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of global optimization Ročník 75; číslo 1; s. 91 - 110
Hlavní autori: Araya, Ignacio, Campusano, Jose, Aliquintui, Damir
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.09.2019
Springer
Springer Nature B.V
Predmet:
Algorithms
Computer Science
Mathematical optimization
Mathematics
Mathematics and Statistics
Nonlinear programming
Operations Research/Decision Theory
Optimization
Real Functions
Solvers
Interval methods
Multiobjective optimization
Branch & bound
ISSN:0925-5001, 1573-2916
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Interval based solvers are commonly used for solving single-objective nonlinear optimization problems. Their reliability and increasing performance make them useful when proofs of infeasibility and/or certification of solutions are a must. On the other hand, there exist only a few approaches dealing with nonlinear optimization problems, when they consider multiple objectives. In this paper, we propose a new interval branch & bound algorithm for solving nonlinear constrained biobjective optimization problems. Although the general strategy is based on other works, we propose some improvements related to the termination criteria, node selection, upperbounding and discarding boxes using the non-dominated set. Most of these techniques use and/or adapt components of IbexOpt, a state-of-the-art interval-based single-objective optimization algorithm. The code of our plugin can be found in our git repository ( https://github.com/INFPUCV/ibex-lib/tree/master/plugins/optim-mop ).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-019-00768-z