On Utilizing Infeasibility in Multiobjective Evolutionary Algorithms
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2009
Typ der Arbeit
Studiengang
Typ
04 - Beitrag Sammelband oder Konferenzschrift
Herausgeber:innen
Barichard, Vincent
Ehrgott, Matthias
Gandibleux, Xavier
T'Kindt, Vincent
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Multiobjective programming and goal programming. Theoretical results and practical applications618
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
113-122
Patentnummer
Verlag / Herausgebende Institution
Springer
Verlagsort / Veranstaltungsort
Berlin
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
In this article, we consider the problem of infeasible solutions (i.e. solutions which violate one or several restrictions of an optimization problem) which can hardly be avoided when new solutions are generated by stochastic and other means during the run of an optimization algorithm. Since typical approaches for dealing with infeasibility such as using a repair mechanism, a punishment approach, or a simple recalculation of solutions are not fully satisfying in many problems, we suggest a new approach of tolerating and actively using infeasible solutions within the framework of multiobjective evolutionary algorithms.
The novel evolutionary algorithm allows solving a multiobjective optimization problem (MOP) with continuous variables by approximating the efficient set. The algorithm uses populations of variable size and new rules for selecting solutions for the subsequent generations. In particular, some of the selected solutions may be infeasible such that the Pareto front is approached at the same time from two sides, the feasible set and a subset of the infeasible set. Since the considered in feasible solutions correspond to a dual optimization problem, we call the new algorithm primaldual multiobjective optimization algorithm, or PDMOEA. The algorithm is demonstrated by considering a numerical test problem and is compared with two other approaches for dealing with infeasibility. The example shows a specific strength of the new approach: By tunneling through infeasible regions, the population may more easily extent to new separated parts of the Pareto set.
Schlagwörter
Feasibility, Multiobjective optimization, constrained optimization, efficient set, evolutionary algorithm, infeasible solutions, optimization problems
Fachgebiet (DDC)
330 - Wirtschaft
005 - Computer Programmierung, Programme und Daten
005 - Computer Programmierung, Programme und Daten
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
978-3-540-85646-7-11
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Unbekannt
Publikationsstatus
Veröffentlicht
Begutachtung
Keine Begutachtung
Open Access-Status
Lizenz
Zitation
HANNE, Thomas, 2009. On Utilizing Infeasibility in Multiobjective Evolutionary Algorithms. In: Vincent BARICHARD, Matthias EHRGOTT, Xavier GANDIBLEUX und Vincent T’KINDT (Hrsg.), Multiobjective programming and goal programming. Theoretical results and practical applications618. Berlin: Springer. 2009. S. 113–122. ISBN 978-3-540-85646-7-11. Verfügbar unter: https://doi.org/10.26041/fhnw-3110