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On Optimization of Automation Systems: Integrating Modular Learning and Optimization
Chalmers University of Technology, Sweden.
RISE Research Institutes of Sweden, Safety and Transport, Electrification and Reliability.ORCID iD: 0000-0002-1559-7896
Chalmers University of Technology, Sweden.
Chalmers University of Technology, Sweden.
2022 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 19, no 3, p. 1662-1674Article in journal (Refereed) Published
Abstract [en]

Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. Vol. 19, no 3, p. 1662-1674
Keywords [en]
Automata, Automation, control systems, Learning automata, learning automata., Multiprotocol label switching, Optimization, Software algorithms, Task analysis, Discrete event simulation, Finite automata, Job analysis, Automaton, Learning automaton., Modular learning, Modular optimizations, Optimisations
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-59065DOI: 10.1109/TASE.2022.3144230Scopus ID: 2-s2.0-85126294749OAI: oai:DiVA.org:ri-59065DiVA, id: diva2:1652033
Available from: 2022-04-14 Created: 2022-04-14 Last updated: 2023-03-23Bibliographically approved

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Farooqui, Ashfaq

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