Metaheuristics "In the Large"

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dc.contributor.authorSwan, J.
dc.contributor.authorAdriaensen, S.
dc.contributor.authorBrownlee, A.E.I.
dc.contributor.authorHammond, K.
dc.contributor.authorJohnson, C.G.
dc.contributor.authorKheiri, A.
dc.contributor.authorKrawiec, F.
dc.contributor.authorMerelo, J.J.
dc.contributor.authorMinku, L.L.
dc.contributor.authorÖzcan, E.
dc.contributor.authorPappa, G.L.
dc.contributor.authorGarcía-Sánchez, P.
dc.contributor.authorSörensen, K.
dc.contributor.authorVoß, S.
dc.contributor.authorWagner, M.
dc.contributor.authorWhite, D.R.
dc.date.issued2022
dc.description.abstractFollowing 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.
dc.description.statementofresponsibilityJerry Swan, Steven Adriaensen, Alexander E.I. Brownlee, Kevin Hammond, Colin G. Johnson, Ahmed Kheiri, Faustyna Krawiec, J.J. Merelo, Leandro L. Minku, Ender Özcan, Gisele L. Pappa, Pablo García-Sánchez, Kenneth Sörensen, Stefan Voß, Markus Wagner, David R. White
dc.identifier.citationEuropean Journal of Operational Research, 2022; 297(2):393-406
dc.identifier.doi10.1016/j.ejor.2021.05.042
dc.identifier.issn0377-2217
dc.identifier.issn1872-6860
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]
dc.identifier.urihttps://hdl.handle.net/2440/133634
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100850
dc.relation.granthttp://purl.org/au-research/grants/arc/DP200102364
dc.rights©2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1016/j.ejor.2021.05.042
dc.subjectEvolutionary Computation; operational research; heuristic design; heuristic methods; architecture; frameworks; interoperability
dc.titleMetaheuristics "In the Large"
dc.typeJournal article
pubs.publication-statusPublished

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