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Metaheuristics “In the Large”

Author

Listed:
  • Swan, Jerry
  • Adriaensen, Steven
  • Brownlee, Alexander E.I.
  • Hammond, Kevin
  • Johnson, Colin G.
  • Kheiri, Ahmed
  • Krawiec, Faustyna
  • Merelo, J.J.
  • Minku, Leandro L.
  • Özcan, Ender
  • Pappa, Gisele L.
  • García-Sánchez, Pablo
  • Sörensen, Kenneth
  • Voß, Stefan
  • Wagner, Markus
  • White, David R.

Abstract

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics “In the Large” project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.

Suggested Citation

  • Swan, Jerry & Adriaensen, Steven & Brownlee, Alexander E.I. & Hammond, Kevin & Johnson, Colin G. & Kheiri, Ahmed & Krawiec, Faustyna & Merelo, J.J. & Minku, Leandro L. & Özcan, Ender & Pappa, Gisele L, 2022. "Metaheuristics “In the Large”," European Journal of Operational Research, Elsevier, vol. 297(2), pages 393-406.
    • Handle: RePEc:eee:ejores:v:297:y:2022:i:2:p:393-406
    DOI: 10.1016/j.ejor.2021.05.042
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    1. Ahmed Kheiri, 2020. "Heuristic Sequence Selection for Inventory Routing Problem," Transportation Science, INFORMS, vol. 54(2), pages 302-312, March.
    2. Addis, Bernardetta & Carello, Giuliana & Ceselli, Alberto, 2013. "Combining very large scale and ILP based neighborhoods for a two-level location problem," European Journal of Operational Research, Elsevier, vol. 231(3), pages 535-546.
    3. Raidl, Günther R., 2015. "Decomposition based hybrid metaheuristics," European Journal of Operational Research, Elsevier, vol. 244(1), pages 66-76.
    4. Fuellerer, Guenther & Doerner, Karl F. & Hartl, Richard F. & Iori, Manuel, 2010. "Metaheuristics for vehicle routing problems with three-dimensional loading constraints," European Journal of Operational Research, Elsevier, vol. 201(3), pages 751-759, March.
    5. Goh, Say Leng & Kendall, Graham & Sabar, Nasser R., 2017. "Improved local search approaches to solve the post enrolment course timetabling problem," European Journal of Operational Research, Elsevier, vol. 261(1), pages 17-29.
    6. Dennis Weyland, 2010. "A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 1(2), pages 50-60, April.
    7. Soria-Alcaraz, Jorge A. & Ochoa, Gabriela & Sotelo-Figeroa, Marco A. & Burke, Edmund K., 2017. "A methodology for determining an effective subset of heuristics in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 260(3), pages 972-983.
    8. Ahmed, Leena & Mumford, Christine & Kheiri, Ahmed, 2019. "Solving urban transit route design problem using selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 274(2), pages 545-559.
    9. Taylor, Simon J.E., 2019. "Distributed simulation: state-of-the-art and potential for operational research," European Journal of Operational Research, Elsevier, vol. 273(1), pages 1-19.
    10. Agarwal, Anurag & Colak, Selcuk & Eryarsoy, Enes, 2006. "Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach," European Journal of Operational Research, Elsevier, vol. 169(3), pages 801-815, March.
    11. 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.
    12. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    13. Graham Kendall & Ruibin Bai & Jacek Błazewicz & Patrick De Causmaecker & Michel Gendreau & Robert John & Jiawei Li & Barry McCollum & Erwin Pesch & Rong Qu & Nasser Sabar & Greet Vanden Berghe , 2016. "Good Laboratory Practice for optimization research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 676-689, April.
    14. Hackney, Ray & Xu, Huinan & Ranchhod, Ashok, 2006. "Evaluating Web Services: Towards a framework for emergent contexts," European Journal of Operational Research, Elsevier, vol. 173(3), pages 1161-1174, September.
    15. Roberto Battiti & Giampietro Tecchiolli, 1994. "The Reactive Tabu Search," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 126-140, May.
    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. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    18. Qu, Rong & Burke, Edmund K. & McCollum, Barry, 2009. "Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems," European Journal of Operational Research, Elsevier, vol. 198(2), pages 392-404, October.
    19. Kheiri, Ahmed & Özcan, Ender, 2016. "An iterated multi-stage selection hyper-heuristic," European Journal of Operational Research, Elsevier, vol. 250(1), pages 77-90.
    20. Nikzad, Erfaneh & Bashiri, Mahdi & Abbasi, Babak, 2021. "A matheuristic algorithm for stochastic home health care planning," European Journal of Operational Research, Elsevier, vol. 288(3), pages 753-774.
    21. Burke, Edmund K. & McCollum, Barry & Meisels, Amnon & Petrovic, Sanja & Qu, Rong, 2007. "A graph-based hyper-heuristic for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 177-192, January.
    22. Drake, John H. & Kheiri, Ahmed & Özcan, Ender & Burke, Edmund K., 2020. "Recent advances in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 405-428.
    23. Pellerin, Robert & Perrier, Nathalie & Berthaut, François, 2020. "A survey of hybrid metaheuristics for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 280(2), pages 395-416.
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