Bringing the power of explicit modelling to optimisation and search
Automated optimisation techniques are central to many engineering disciplines, where they provide an excellent tool for design space exploration and finding the best design for a given set of requirements. Automation allows to explore many more potential solutions than what would be manually feasible for human engineers. As a result, much better (and sometimes surprising) solutions can be found using automated optimisation techniques than could be found by human engineers. A recent interesting application domain (one of many) is quantum computing, where the creation of programs can be effectively reframed as an optmisation problem.
In the context of digital twins there are even more applications of optimisation:
if-what analysis uses search-based optimisation to find the optimal set of changes to a real-world asset or the optimal set of changes to a real-world process to achieve the best possible outcome (e.g., least cost, least risk, best product quality etc.).
search-based calibration uses parameter sweeps and searches over model structures to match a simulation / digital twin as closely as possible to the reality it represents.
generative social science builds simulation models to explore possible mechanisms that can explain observed social phenomena. It uses forms of search-based calibration to explore the space of possible mechanisms.
But using optimisation algorithms and tools well is difficult and requires a broad range of technical expertise. Model-driven engineering can help here by providing tools to make the description and execution of optimisation problems easier, including tieing them into the modelling and simulation capabilities of digital twins. We have developed the MDEOptimiser tool as a research foundation for exploring ways in which MDE can help with optimisation problems.
Search-based optimisation, one of the most flexible optimisation approaches, requires the definition of good problem encodings and mutation operators. We have found that model-driven engineering provides key benefits here by allowing encodings and smart mutation operators to be defined easily and efficiently, and in some cases even for optimal mutation operators to be generated automatically.
More research is required across the board, including in new application areas, new mechanisms for efficient encodings, mechanisms for hiding more of the technical complexity, or techniques for generating smarter, more efficient search operators and algorithms.
@article{HorcasEtAl22,author={Horcas, Jose-Miguel and Str{\"u}ber, Daniel and Burdusel, Alexandru and Martinez, Jabier and Zschaler, Steffen},title={We're Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration},journal={IEEE Transactions on Software Engineering},pages={1102--1117},volume={49},number={3},year={2023},doi={10.1109/TSE.2022.3171404},url={https://dx.doi.org/10.1109/TSE.2022.3171404},}
@article{BurduselEtAl21,author={Burdusel, Alexandru and Zschaler, Steffen and John, Stefan},title={Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering},journal={Software and Systems Modelling},year={2021},doi={10.1007/s10270-021-00914-w},}
@inproceedings{HorcasEtAl22a,author={Horcas, Jose-Miguel and Str{\"u}ber, Daniel and Burdusel, Alexandru and Martinez, Jabier and Zschaler, Steffen},title={Extended Abstract: We're Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration},booktitle={Journal First track at SPLC'22},year={2022},}
@inproceedings{JohnEtAl19,author={John, Stefan and Burdusel, Alexandru and Bill, Robert and Str{\"u}ber, Daniel and Taentzer, Gabriele and Zschaler, Steffen and Wimmer, Manuel},title={Searching for Optimal Models: Comparing Two Encoding Approaches},booktitle={Proc. 12th Int'l Conf. Model Transformations (ICMT'19)},year={2019},}
@inproceedings{BurduselEtAl19,author={Burdusel, Alexandru and Zschaler, Steffen and John, Stefan},title={Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering},booktitle={IEEE / ACM 22nd Int'l Conf. Model Driven Engineering Languages and Systems (MODELS'19)},year={2019},}
2018
MDEoptimiser: A Search Based Model Engineering Tool
Alexandru Burdusel, Steffen Zschaler, and Daniel Strüber
In Companion Proc. 21st ACM/IEEE Int’l Conf Model Driven Engineering Languages and Systems, 2018
@inproceedings{BurduselEtAl18,author={Burdusel, Alexandru and Zschaler, Steffen and Str\"{u}ber, Daniel},title={{MDEoptimiser}: A Search Based Model Engineering Tool},pages={12--16},numpages={5},url={http://doi.acm.org/10.1145/3270112.3270130},doi={10.1145/3270112.3270130},acmid={3270130},keywords={model driven engineering, search based model engineering, search based optimisation, search based software engineering},booktitle={Companion Proc. 21st ACM/IEEE Int'l Conf Model Driven Engineering Languages and Systems},series={MODELS '18},year={2018},isbn={978-1-4503-5965-8},location={Copenhagen, Denmark},publisher={ACM},address={New York, NY, USA},}
Henshin: A Model Transformation Language and its Use for Search-Based Model Optimisation in MDEOptimiser
Daniel Strüber, Alexandru Burdusel, Stefan John, and 1 more author
@inproceedings{StrueberEtAl18,author={Str{\"u}ber, Daniel and Burdusel, Alexandru and John, Stefan and Zschaler, Steffen},title={Henshin: A Model Transformation Language and its Use for Search-Based Model Optimisation in {MDEOptimiser}},booktitle={Modellierung 2018, Tutorials},year={2018},}
Deriving Persuasion Strategies Using Search-Based Model Engineering
Josh Murphy, Alexandru Burdusel, Michael Luck, and 2 more authors
In 7th International Conference on Computational Models of Argument (COMMA’18), 2018
@inproceedings{MurphyEtAl18,author={Murphy, Josh and Burdusel, Alexandru and Luck, Michael and Zschaler, Steffen and Black, Elizabeth},title={Deriving Persuasion Strategies Using Search-Based Model Engineering},booktitle={7th International Conference on Computational Models of Argument (COMMA'18)},year={2018},}
2017
Automatic generation of evolution rules for model-driven optimisation
Alexandru Burdusel, and Steffen Zschaler
In 8th International Workshop on Graph Computation Models (GCM’17), 2017
@inproceedings{BurduselZschaler17,author={Burdusel, Alexandru and Zschaler, Steffen},title={Automatic generation of evolution rules for model-driven optimisation},booktitle={8th International Workshop on Graph Computation Models (GCM'17)},year={2017},}
2016
Mejora de una representación genética genérica para modelos
Lorenzo Mandow, Jose Antonio Montenegro, and Steffen Zschaler
In Actas de la XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA’16), 2016
@inproceedings{MandowEtAl16,author={Mandow, Lorenzo and Montenegro, Jose Antonio and Zschaler, Steffen},title={Mejora de una representaci{\'o}n gen{\'e}tica gen{\'e}rica para modelos},booktitle={Actas de la XVII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA'16)},year={2016},}
Towards Model-Based Optimisation: Using domain knowledge explicitly
Steffen Zschaler, and Lawrence Mandow
In Proc. Workshop on Model-Driven Engineering, Logic and Optimization (MELO’16), 2016
@inproceedings{BurduselZschaler16,author={Burdusel, Alexandru and Zschaler, Steffen},title={Model Optimisation for Feature--Class allocation using {MDEOptimiser}: A {TTC} 2016 Submission},booktitle={Transformation Tool Contest},year={2016},}
Technical Reports
2019
Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering.
Alexandru Burdusel, Steffen Zschaler, and Stefan John
@techreport{BurduselEtAl19a,author={Burdusel, Alexandru and Zschaler, Steffen and John, Stefan},title={Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering.},howpublished={Technical Report, arXiv:1907.05647 [cs.AI]},year={2019},}