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A general branch-and-bound framework for continuous global multiobjective optimization

Eichfelder, Gabriele; Kirst, Peter; Meng, Laura; Stein, Oliver

Abstract:
Current generalizations of the central ideas of single-objective branch-and-bound to the multiobjective setting do not seem to follow their train of thought all the way. The present paper complements the various suggestions for generalizations of partial lower bounds and of overall upper bounds by general constructions for overall lower bounds from partial lower bounds, and by the corresponding termination criteria and node selection steps. In particular, our branch-and-bound concept employs a new enclosure of the set of nondominated points by a union of boxes. On this occasion we also suggest a new discarding test based on a linearization technique. We provide a convergence proof for our general branch-and-bound framework and illustrate the results with numerical examples.

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Verlagsausgabe §
DOI: 10.5445/IR/1000130839
Veröffentlicht am 23.03.2021
Originalveröffentlichung
DOI: 10.1007/s10898-020-00984-y
Web of Science
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Operations Research (IOR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0925-5001, 1573-2916
KITopen-ID: 1000130839
Erschienen in Journal of global optimization
Verlag Springer
Vorab online veröffentlicht am 19.01.2021
Nachgewiesen in Scopus
Web of Science
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