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21-09-2018 | Methodologies and Application | Issue 19/2019

Soft Computing 19/2019

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

Journal:
Soft Computing > Issue 19/2019
Authors:
Ahmad Alhindi, Abrar Alhindi, Atif Alhejali, Abdullah Alsheddy, Nasser Tairan, Hosam Alhakami
Important notes
Communicated by V. Loia.

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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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