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Metaheuristics “In the Large”

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • Jerry 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
<mark>Journal publication date</mark>6/06/2021
<mark>Journal</mark>European Journal of Operational Research
Number of pages14
Publication StatusE-pub ahead of print
Early online date6/06/21
<mark>Original language</mark>English


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.