Enhancing MOEA/D with Learning: Application to Routing Problems with Time Windows

Legrand, Clément; Cattaruzza, Diego; Jourdan, Laetitia; Kessaci, Marie-Eléonore

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

DOI :

10.1145/3520304.3528909

Title :

Enhancing MOEA/D with Learning: Application to Routing Problems with Time Windows

Author(s) :

Legrand, Clément [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-Eléonore [Auteur]
Operational Research, Knowledge And Data [ORKAD]

Conference title :

GECCO '22: Genetic and Evolutionary Computation Conference

City :

Boston

Country :

Etats-Unis d'Amérique

Start date of the conference :

2022-07-09

Publication date :

2022-07-09

English keyword(s) :

Machine Learning
Hybridization
Multi-objective optimization
Operations research
Routing and layout

HAL domain(s) :

Informatique [cs]

English abstract : [en]

Integrating machine learning (ML) techniques into metaheuristics is an efficient approach in single-objective optimization. Indeed, high-quality solutions often contain relevant knowledge, that can be used to guide the ...
Show more >Integrating machine learning (ML) techniques into metaheuristics is an efficient approach in single-objective optimization. Indeed, high-quality solutions often contain relevant knowledge, that can be used to guide the heuristic towards promising areas. In multiobjective optimization, the quality of solutions is evaluated according to multiple criteria that are generally conflicting. Therefore, the ML techniques designed for single-objective optimization can not be directly adapted for multi-objective optimization. In this paper, we propose to enhance the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) with a clustering-based learning mechanism. To be more precise, solutions are grouped regarding a metric based on their quality on each criterion, and the knowledge from the solutions of the same group is merged. Experiments are conducted on the multi-objective vehicle routing problem with time windows. The results show that MOEA/D with learning outperforms the original version.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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