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Soft Computing
Published in: Soft Computing 19/2019

21-09-2018 | Methodologies and Application

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

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

Published in: Soft Computing | Issue 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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Footnotes
Literature
Alhindi A, Zhang Q (2014) MOEA/D with Tabu search for multiobjective permutation flow shop scheduling problems. In: Proceedings of the IEEE world congress on evolutionary computation (CEC). IEEE, Beijing, pp 1155–1164
Alhindi A, Zhang Q, Tsang E (2014) Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation. The 14th UK workshop on computational intelligence (UKCI). IEEE, Bradford, pp 1155–1164
Alsheddy A (2011) Empowerment scheduling : a multi-objective optimization approach using guided local search. Ph.D thesis, University of Essex, Essex
Alsheddy A, Tsang E (2010) Guided pareto local search based frameworks for biobjective optimization. In: IEEE congress on evolutionary computation (CEC). IEEE, Shanghai, pp 1–8
Hansen MP, Jaszkiewicz A (1998) Evaluating the quality of approximations to the non-dominated set. Technical University of Denmark, Department of Mathematical Modelling, Lyngby
Ke L, Zhang Q, Battiti R (2013) MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans Cybern 43(6):1845–1959CrossRef
Li H, Landa-Silva D (2011) An adaptive evolutionary multi-objective approach based on simulated annealing. Evolut Comput 19(4):561–595CrossRef
Oliver I, Smith D, Holland J (1987) A study of permutation crossover operators on the TSP, genetic algorithms and their applications. In: Proceedings of the second international conference on genetic algorithms, Hillsdale, pp 224–230
Tairan N, Zhang Q (2010) Population-based guided local search: some preliminary experimental results. In: IEEE congress on evolutionary computation (CEC). IEEE, Shanghai, pp 1–5
Tairan N, Zhang Q (2011) P-GLS-II: an enhanced version of the population-based guided local search. In: Proceedings of the 13th annual conference on genetic and evolutionary computation. ACM, Dublin, pp 537–544
Voudouris C (1997) Guided local search for combinatorial optimisation problems. Ph.D thesis, University of Essex, Essex
Voudouris C, Tsang E, Alsheddy A (2010) Guided local search. In: Handbook of metaheuristics, Springer, pp 321–361
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731CrossRef
Metadata
Title
MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search
Authors
Ahmad Alhindi
Abrar Alhindi
Atif Alhejali
Abdullah Alsheddy
Nasser Tairan
Hosam Alhakami
Publication date
21-09-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 19/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3524-z

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