On Optimization of Automation Systems: Integrating Modular Learning and Optimization
Artikel i vetenskaplig tidskrift, 2022

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.

Automata

Software algorithms

control systems

Learning automata

Multiprotocol label switching

optimization

Automation

Optimization

learning automata.

Task analysis

Automation

Författare

Fredrik Hagebring

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Ashfaq Hussain Farooqui

RISE Research Institutes of Sweden

Martin Fabian

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Bengt Lennartson

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Transactions on Automation Science and Engineering

1545-5955 (ISSN)

Vol. In Press

Ämneskategorier

Reglerteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/TASE.2022.3144230

Mer information

Senast uppdaterat

2022-03-24