Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Metaheuristics “In the Large”
AU - Swan, Jerry
AU - Adriaensen, Steven
AU - Brownlee, Alexander E.I.
AU - Hammond, Kevin
AU - Johnson, Colin G.
AU - Kheiri, Ahmed
AU - Krawiec, Faustyna
AU - Merelo, J.J.
AU - Minku, Leandro L.
AU - Özcan, Ender
AU - Pappa, Gisele L.
AU - García-Sánchez, Pablo
AU - Sörensen, Kenneth
AU - Voß, Stefan
AU - Wagner, Markus
AU - White, David R.
PY - 2022/3/31
Y1 - 2022/3/31
N2 - 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.
AB - 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.
KW - Evolutionary Computation
KW - Operational Research
KW - Heuristic design
KW - Heuristic methods
KW - Architecture
KW - Frameworks
KW - Interoperability
U2 - 10.1016/j.ejor.2021.05.042
DO - 10.1016/j.ejor.2021.05.042
M3 - Journal article
VL - 297
SP - 393
EP - 406
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 2
ER -