Titre :

Applying Ising machines to multi-objective QUBOs

Auteur(s) :

Ayodele, Mayowa [Auteur]
Fujitsu Laboratories of Europe Ltd.
Allmendinger, Richard [Auteur]
University of Manchester [Manchester]
López-Ibáñez, Manuel [Auteur]
University of Manchester [Manchester]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parizy, Matthieu [Auteur]
Fujitsu Laboratories Ltd.

Titre de la manifestation scientifique :

GECCO 2023 - Genetic and Evolutionary Computation Conference Companion

Ville :

Lisbon

Pays :

Portugal

Date de début de la manifestation scientifique :

2023-07-15

Titre de la revue :

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Éditeur :

ACM

Date de publication :

2023-07

Mot(s)-clé(s) en anglais :

Multi-objective optimisation
QUBO
UBQP
Aggregation
Scalarisation
Digital annealer

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

Multi-objective optimisation problems involve finding solutions with varying trade-offs between multiple and often conflicting objectives. Ising machines are physical devices that aim to find the absolute or approximate ...
Lire la suite >Multi-objective optimisation problems involve finding solutions with varying trade-offs between multiple and often conflicting objectives. Ising machines are physical devices that aim to find the absolute or approximate ground states of an Ising model. To apply Ising machines to multi-objective problems, a weighted sum objective function is used to convert multi-objective into singleobjective problems. However, deriving scalarisation weights that archives evenly distributed solutions across the Pareto front is not trivial. Previous work has shown that adaptive weights based on dichotomic search, and one based on averages of previously explored weights can explore the Pareto front quicker than uniformly generated weights. However, these adaptive methods have only been applied to bi-objective problems in the past. In this work, we extend the adaptive method based on averages in two ways: (i) we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii) we use an alternative measure of distance to improve performance. We compare the proposed method with existing ones and show that it leads to the best performance on multi-objective Unconstrained Binary Quadratic Programming (mUBQP) instances with 3 and 4 objectives and that it is competitive with the best one for instances with 2 objectives.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL