Tiwonge Msulira Banda, Ciprian Zavoianu, Andrei Petrovski, Daniel Wöckinger, Gerd Bramerdorfer,
"Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D."
: Proceedings of the 25th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC 2023), 2024
Original Titel:
Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D.
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 25th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC 2023)
Original Kurzfassung:
For engineers to create durable and effective electri
cal assemblies, modelling and controlling heat transfer in rotating
electrical machines (such as motors) is crucial. In this paper, we
compare the performance of three multi-objective evolutionary
algorithms, namely NSGA-II, NSGA-III, and MOEA/D in find
ing the best trade-offs between data collection costs/effort and
expected modelling errors when creating low-complexity Linear
Regression (LR) models that can accurately estimate key motor
component temperatures under various operational scenarios.
The algorithms are integrated into a multi-objective thermal
modelling strategy that aims to guide the discovery of models
that are suitable for microcontroller deployment. Our findings
show that while NSGA-II and NSGA-III yield comparably good
optimisation outcomes, with a slight, but statistically significant
edge for NSGA-II, the results achieved by MOEA/D for this use
case are below par.
Forschungs-