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Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms. (2021)
Presentation / Conference
HAN, K., CHRISTIE, L.A., ZĂVOIANU, A.-C. and MCCALL, J. 2021. Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms. Presented at 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference].

The past five years have seen a rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. Using a real-world scenario from the Leeds Metropolitan Area as a c... Read More about Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms..

Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems. (2021)
Conference Proceeding
LIEFOOGHE, A., VEREL, S., LACROIX, B., ZĂVOIANU, A.-C. and MCCALL, J. 2021. Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems. In Chicano, F. (ed) Proceedings of 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference]. New York: ACM [online], pages 421-429. Available from: https://doi.org/10.1145/3449639.3459353

In this paper, we demonstrate the application of features from landscape analysis, initially proposed for multi-objective combinatorial optimisation, to a benchmark set of 1 200 randomly-generated multiobjective interpolated continuous optimisation p... Read More about Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems..

A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production. (2020)
Journal Article
ZĂVOIANU, A.-C., LUGHOFER, E., POLLAK, R., EITZINGER, C. and RADAUER, T. 2021. A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production. Applied soft computing [online], 98, article ID 106827. Available from: https://doi.org/10.1016/j.asoc.2020.106827

We describe a general strategy for optimizing the quality of products of industrial batch processes that comprise multiple production stages. We focus on the particularities of applying this strategy in the field of micro-fluidic chip production. Our... Read More about A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production..

Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. (2020)
Conference Proceeding
ZĂVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2020. Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, The Netherlands. Lecture notes in computer science, 12269. Cham; Springer, part 1, pages 287-300. Available from: https://doi.org/10.1007/978-3-030-58112-1_20

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time converge... Read More about Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems..

Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design. (2020)
Conference Proceeding
CERNUDA, C., LLAVORI, I., ZĂVOIANU, A.-C., AGUIRRE, A., ZABALA, A. and PLAZA, J. 2020. Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design. In Proceedings of 25th Institute of Electrical and Electronics Engineers (IEEE) Emerging technologies and factory automation international conference 2020 (ETFA 2020), 8-11 September 2020, Vienna, Austria. Piscataway: IEEE [online], article ID 9212166, pages 829-836. Available from: https://doi.org/10.1109/ETFA46521.2020.9212166

This work presents a critical analysis of the suitability of surrogate models for finite element method application. A case study of a finite element method (FEM) structural problem was selected in order to test the performance of surrogate algorithm... Read More about Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design..

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. (2020)
Journal Article
LUGHOFER, E., ZAVOIANU, A.-C., POLLAK, R., PRATAMA, M., MEYER-HEYE, P., ZÖRRER, H., EITZINGER, C. and RADAUER, T. 2020. On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. Information sciences [online], 537, 425-451. Available from: https://doi.org/10.1016/j.ins.2020.06.034

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detecti... Read More about On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks..

On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks. (2020)
Conference Proceeding
ZĂVOIANU, A.-C., KITZBERGER, M., BRAMERDORFER, G. and SAMINGER-PLATZ, S. 2020. On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: EUROCAST 2019: revised selected papers from the proceedings of the 17th International conference on computer aided systems theory (EUROCAST 2019), 17-22 February 2019, Las Palmas de Gran Canaria, Spain. Lecture notes in computer science, 12013. Cham: Springer [online], part 1, pages 319-326. Available from: https://doi.org/10.1007/978-3-030-45093-9_39

We describe initial attempts to model the dynamic thermal behavior of electrical machines by evaluating the ability of linear and non-linear (regression) modeling techniques to replicate the performance of simulations carried out using a lumped param... Read More about On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks..