Enhancing MOEA/D with Learning: Application to Routing Problems with Time Windows
| Autor: | Clément Legrand, Diego Cattaruzza, Laetitia Jourdan, Marie-Eléonore Kessaci |
|---|---|
| Přispěvatelé: | Operational Research, Knowledge And Data (ORKAD), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Integrated Optimization with Complex Structure (INOCS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université libre de Bruxelles (ULB)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | GECCO 2022-The Genetic and Evolutionary Computation Conference GECCO 2022-The Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. ⟨10.1145/3520304.3528909⟩ |
| DOI: | 10.1145/3520304.3528909⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|