TIWONGE BANDA's Outputs (3)

A dominance-based surrogate classifier for multi-objective evolutionary algorithms. (2024)
Presentation / Conference Contribution
BANDA, T.M. and ZĂVOIANU, A.-C. 2025. A dominance-based surrogate classifier for multi-objective evolutionary algorithms. In Bramer, M. and Stahl, F. (eds.) Artificial Intelligence XLI: proceedings of the 44th SGAI (Specialist Group on Artificial Intelligence) International conference on artificial intelligence 2024 (AI 2024), 17-19 December 2024, Cambridge, UK. Lecture notes in computer science, 15446. Cham: Springer [online], part I, pages 268-281. Available from: https://doi.org/10.1007/978-3-031-77915-2_19

The application of Multi-Objective Evolutionary Algorithms (MOEAs) is often constrained when addressing computationally expensive Multi-Objective Optimisation Problems (MOOPs). To mitigate this, we propose a dominance-based surrogate classifier that... Read More about A dominance-based surrogate classifier for multi-objective evolutionary algorithms..

Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. (2023)
Presentation / Conference Contribution
BANDA, T.M., ZĂVOIANU, A.-C., PETROVSKI, A., WÖCKINGER, D. and BRAMERDORFER, G. 2024. Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. In Stratulat, S., Marin, M., Negru, V. and Zaharie, D. (eds.) Proceedings of the 25th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC 2023), 11-14 September 2023, Nancy, France. Los Alamitos: IEEE Computer Society [online], pages 186-193. Available from: https://doi.org/10.1109/SYNASC61333.2023.00032

For engineers to create durable and effective electrical 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... Read More about Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D..

A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines. (2023)
Journal Article
BANDA, T.M., ZĂVOIANU, A.-C., PETROVSKI, A., WÖCKINGER, D. and BRAMERDORFER, G. 2024. A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines. ACM transactions on evolutionary learning and optimization [online], 4(1), article number 3. Available from: https://doi.org/10.1145/3597618

Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving... Read More about A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines..