The optimisation algorithm library aims to make optimisation algorithms easier to find by collecting them, annotated with information about high level properties they can handle like the type of decision variables, number of objectives, and the presence of constraints. Where available references to papers describing the algorithm and implementations are included. All collected algorithms are available through a searchable website.
In addition to the algorithm collection of OAL, we are also working on libraries for optimisation problems and features:
- OPL - Optimisation problem library: website, github
- OFL - Optimisation feature library: website, github
Finally, an overview of the different projects is available here.
Contributions and corrections are very welcome, through pull requests, issue reporting, or email (see contact).
Want to get involved to make OAL even better? Any help is welcome!
We need help to
- Extend the OAL infrastructure to make it easier to add, find, edit, extend, aggregate algorithms and their description
- Advertise OAL
- and, of course, add more algorithms!
Finally, you are also very welcome to propose your own ideas to make OAL better, and work on those.
To get involved, just send an email (see contact), or talk to any of us.
@misc{oal26,
title = {{OAL} – Optimisation algorithm library},
author = {Koen van der Blom and Diederick Vermetten and Vanessa Volz},
year = {2026},
note = {\url{https://openoptimizationorg.github.io/OAL/}}
}