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

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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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Notes

  1. http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95.

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Acknowledgements

The authors would like to thank the Associate Editor and anonymous reviewers for their valuable comments, which greatly improved the quality of this paper. The authors also wish to acknowledge the help and corporation of the Hybrid Metaheuristic Computing research group, King Khalid University (KKU), Kingdom of Saudi Arabia.

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Authors and Affiliations

  1. College of Computer & Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia

    Ahmad Alhindi, Atif Alhejali & Hosam Alhakami

  2. Baiyna AI Research Lab, Makkah, Saudi Arabia

    Abrar Alhindi

  3. College of Computer and Information Sciences, Al-Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

    Abdullah Alsheddy

  4. College of Computer Science, King Khalid University, Abha, Asir, Saudi Arabia

    Nasser Tairan

Authors
  1. Ahmad Alhindi
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  2. Abrar Alhindi
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  3. Atif Alhejali
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  4. Abdullah Alsheddy
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  5. Nasser Tairan
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  6. Hosam Alhakami
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Correspondence to Ahmad Alhindi.

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Communicated by V. Loia.

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Alhindi, A., Alhindi, A., Alhejali, A. et al. MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search. Soft Comput 23, 9605–9615 (2019). https://doi.org/10.1007/s00500-018-3524-z

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  • DOI: https://doi.org/10.1007/s00500-018-3524-z

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