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Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art

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  • Karimi-Mamaghan, Maryam
  • Mohammadi, Mehrdad
  • Meyer, Patrick
  • Karimi-Mamaghan, Amir Mohammad
  • Talbi, El-Ghazali

Abstract

In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta-heuristics toward an efficient, effective, and robust search and improve their performance in terms of solution quality, convergence rate, and robustness.

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  • Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
    • Handle: RePEc:eee:ejores:v:296:y:2022:i:2:p:393-422
    DOI: 10.1016/j.ejor.2021.04.032
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    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. Maturana, Jorge & Riff, Maria-Cristina, 2007. "Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach," European Journal of Operational Research, Elsevier, vol. 179(3), pages 677-691, June.
    3. Mosadegh, H. & Fatemi Ghomi, S.M.T. & Süer, G.A., 2020. "Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 282(2), pages 530-544.
    4. Zhalechian, M. & Tavakkoli-Moghaddam, R. & Zahiri, B. & Mohammadi, M., 2016. "Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 182-214.
    5. Corne, David & Dhaenens, Clarisse & Jourdan, Laetitia, 2012. "Synergies between operations research and data mining: The emerging use of multi-objective approaches," European Journal of Operational Research, Elsevier, vol. 221(3), pages 469-479.
    6. Wawrzyniak, Jakub & Drozdowski, Maciej & Sanlaville, Éric, 2020. "Selecting algorithms for large berth allocation problems," European Journal of Operational Research, Elsevier, vol. 283(3), pages 844-862.
    7. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
    8. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pirayesh, Amir & Karimi-Mamaghan, Amir Mohammad & Irani, Hassan, 2020. "Hub-and-spoke network design under congestion: A learning based metaheuristic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    9. Mohammadi, Mehrdad & Jula, Payman & Tavakkoli-Moghaddam, Reza, 2019. "Reliable single-allocation hub location problem with disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 90-120.
    10. Yong Deng & Yang Liu & Deyun Zhou, 2015. "An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-6, October.
    11. Gerhard Reinelt, 1991. "TSPLIB—A Traveling Salesman Problem Library," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 376-384, November.
    12. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    13. Bo Peng & Yuan Zhang & Yuvraj Gajpal & Xiding Chen, 2019. "A Memetic Algorithm for the Green Vehicle Routing Problem," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    14. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.
    15. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    16. Martin, Simon & Ouelhadj, Djamila & Beullens, Patrick & Ozcan, Ender & Juan, Angel A. & Burke, Edmund K., 2016. "A multi-agent based cooperative approach to scheduling and routing," European Journal of Operational Research, Elsevier, vol. 254(1), pages 169-178.
    17. Voudouris, Christos & Tsang, Edward, 1999. "Guided local search and its application to the traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 113(2), pages 469-499, March.
    18. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    19. Gao, Shangce & Wang, Yirui & Cheng, Jiujun & Inazumi, Yasuhiro & Tang, Zheng, 2016. "Ant colony optimization with clustering for solving the dynamic location routing problem," Applied Mathematics and Computation, Elsevier, vol. 285(C), pages 149-173.
    20. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Jula, Payman & Pirayesh, Amir & Ahmadi, Hadi, 2020. "A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty," European Journal of Operational Research, Elsevier, vol. 285(2), pages 513-537.
    21. Benlic, Una & Epitropakis, Michael G. & Burke, Edmund K., 2017. "A hybrid breakout local search and reinforcement learning approach to the vertex separator problem," European Journal of Operational Research, Elsevier, vol. 261(3), pages 803-818.
    22. El-Ghazali Talbi, 2016. "Combining metaheuristics with mathematical programming, constraint programming and machine learning," Annals of Operations Research, Springer, vol. 240(1), pages 171-215, May.
    23. Edmund K. Burke & Matthew R. Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2019. "A Classification of Hyper-Heuristic Approaches: Revisited," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 453-477, Springer.
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