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21.09.2018 | Methodologies and Application

MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search

verfasst von: Ahmad Alhindi, Abrar Alhindi, Atif Alhejali, Abdullah Alsheddy, Nasser Tairan, Hosam Alhakami

Erschienen in: Soft Computing | Ausgabe 19/2019

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Abstract

This paper proposes an idea of using well studied and documented single-objective optimization methods in multiobjective evolutionary algorithms. It develops a hybrid algorithm which combines the multiobjective evolutionary algorithm based on decomposition (MOEA/D) with guided local search (GLS), called MOEA/D-GLS. It needs to optimize multiple single-objective subproblems in a collaborative way by defining neighborhood relationship among them. The neighborhood information and problem-specific knowledge are explicitly utilized during the search. The proposed GLS alternates among subproblems to help escape local Pareto optimal solutions. The experimental results have demonstrated that MOEA/D-GLS outperforms MOEA/D on multiobjective traveling salesman problems.

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Titel: MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search
verfasst von: Ahmad Alhindi
Abrar Alhindi
Atif Alhejali
Abdullah Alsheddy
Nasser Tairan
Hosam Alhakami
Publikationsdatum: 21.09.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 19/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3524-z

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