Metaheuristics “In the Large”

Swan, Jerry; Adriaensen, Steven; 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.

Citation

Swan, J., Adriaensen, S., Johnson, C. G., Kheiri, A., Krawiec, F., Merelo, J. J., Minku, L. L., Özcan, E., Pappa, G. L., García-Sánchez, P., Sörensen, K., Voß, S., Wagner, M., & White, D. R. (2022). Metaheuristics “In the Large”. European Journal of Operational Research, 297(2), 393-406. https://doi.org/10.1016/j.ejor.2021.05.042

• Journal Article Type: Review
• Acceptance Date: May 25, 2021
• Online Publication Date: Jun 6, 2021
• Publication Date: Mar 1, 2022
• Deposit Date: Jun 10, 2021
• Publicly Available Date: Jul 12, 2021
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 297
• Issue: 2
• Pages: 393-406
• DOI: https://doi.org/10.1016/j.ejor.2021.05.042
• Keywords: Management Science and Operations Research; Modelling and Simulation; Information Systems and Management; Evolutionary Computation; Operational Research; Heuristic design; Heuristic methods; Architecture Frameworks; Interoperability
• Public URL: https://nottingham-repository.worktribe.com/output/5654960
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0377221721004707
• Additional Information: https://arxiv.org/abs/2011.09821