Integration of Knowledge Discovery into MOEA/D
Clément Legrand  1, @  , Diego Cattaruzza  2, @  , Laetitia Jourdan  3, @  , Marie-Eléonore Kessaci  1, @  
1 : Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Ecole Centrale de Lille, Institut National de Recherche en Informatique et en Automatique, Institut Mines-Télécom [Paris], Université de Lille, Centre National de la Recherche Scientifique : UMR9189
2 : Ecole Centrale de Lille
Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
3 : University of Lille, CRIStAL, UMR 9189, CNRS, Centrale Lille, France
University of Lille, Lille

Extracting knowledge from solutions and then using it to guide the search is a complex task, which has not been highly explored in a discrete multi-objective optimization context. Considering the papers on that subject leads to the following terminology for Knowledge Discovery (KD) processes. A KD process is built upon two main procedures called Knowledge Extraction and Knowledge Injection. The extraction procedure aims to extract problem-related knowledge from one or several solutions.
Then the extracted knowledge can be used by the injection procedure to build new solutions taking into account past iterations.
In this article, we investigate how a KD mechanism can be integrated into MOEA/D, to solve a bi-objective Vehicle Routing Problem with Time Windows (bVRPTW).

Type : : Article
Thématiques : Application et théorie de l’optimisation multi-objectif (GT ATOM)
Mots-Clés : Multi Objective Optimisation ; MOEA/D ; Knowledge Discovery ; Routing
Personnes connectées : 1 Vie privée
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