Investigation of the Benefit of Extracting ...
Type de document :
Communication dans un congrès avec actes
Titre :
Investigation of the Benefit of Extracting Patterns from Local Optima to Solve a Bi-objective VRPTW
Auteur(s) :
Legrand, Clement [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Cattaruzza, Diego [Auteur]
Integrated Optimization with Complex Structure [INOCS]
Jourdan, Laetitia [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Kessaci, Marie-Eleonore [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Operational Research, Knowledge And Data [ORKAD]
Cattaruzza, Diego [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Integrated Optimization with Complex Structure [INOCS]
Jourdan, Laetitia [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Operational Research, Knowledge And Data [ORKAD]
Kessaci, Marie-Eleonore [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
MIC 2024 - Metaheuristics International Conference
Ville :
Lorient
Pays :
France
Date de début de la manifestation scientifique :
2024-06-04
Titre de l’ouvrage :
Metaheuristics International Conference
Titre de la revue :
Lecture Notes in Computer Science
Éditeur :
Springer Nature Switzerland
Lieu de publication :
Cham
Date de publication :
2024-06-17
Mot(s)-clé(s) en anglais :
Combinatorial Optimization
Multi-Objective Optimization
Online Learning
Genetic Algorithm
Local Search
Multi-Objective Optimization
Online Learning
Genetic Algorithm
Local Search
Discipline(s) HAL :
Informatique [cs]
Résumé en anglais : [en]
Hybridizing learning and optimization often improves existing algorithms in single-objective optimization.Indeed, high-quality solutions often contain relevant knowledge that can be used to guide the heuristic towards ...
Lire la suite >Hybridizing learning and optimization often improves existing algorithms in single-objective optimization.Indeed, high-quality solutions often contain relevant knowledge that can be used to guide the heuristic towards promising areas. Learning from the structure of solutions is challenging in combinatorial problems. Most of the time, local optima are considered for this task since they tend to contain more relevant structural information. If local optima generally contain more interesting information than other solutions, producing them requires a time-consuming process. In this paper, we study the benefits of learning from local optima during the execution of a multi-objective algorithm. To this end, we consider a hybridized MOEA/D (a multi-objective genetic algorithm) with a knowledge discovery mechanism adapted to the problem solved and we conductexperiments on a bi-objective vehicle routing problem with time windows. The knowledge discovery mechanism extracts sequences of customers from solutions. The results show the benefit of using different strategies for the components of the knowledge discovery mechanism and the efficacy of extracting patterns from local optima for larger instances. An analysis of speed-up performance gives deeper conclusions about the use of local optima.Lire moins >
Lire la suite >Hybridizing learning and optimization often improves existing algorithms in single-objective optimization.Indeed, high-quality solutions often contain relevant knowledge that can be used to guide the heuristic towards promising areas. Learning from the structure of solutions is challenging in combinatorial problems. Most of the time, local optima are considered for this task since they tend to contain more relevant structural information. If local optima generally contain more interesting information than other solutions, producing them requires a time-consuming process. In this paper, we study the benefits of learning from local optima during the execution of a multi-objective algorithm. To this end, we consider a hybridized MOEA/D (a multi-objective genetic algorithm) with a knowledge discovery mechanism adapted to the problem solved and we conductexperiments on a bi-objective vehicle routing problem with time windows. The knowledge discovery mechanism extracts sequences of customers from solutions. The results show the benefit of using different strategies for the components of the knowledge discovery mechanism and the efficacy of extracting patterns from local optima for larger instances. An analysis of speed-up performance gives deeper conclusions about the use of local optima.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :