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Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. (2024)
Journal Article
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J.A.W. 2024. Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. IEEE transactions on intelligent transportation systems, [online], Early Access. Available from: https://doi.org/10.1109/TITS.2024.3374550

The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transp... Read More about Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms..

Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. (2023)
Conference Proceeding
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..

Explaining a staff rostering problem by mining trajectory variance structures. (2023)
Conference Proceeding
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., ZĂVOIANU, A.-C., BROWNLEE, A.E.I. and AINSLIE, R. 2023. Explaining a staff rostering problem by mining trajectory variance structures. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI international conference on artificial intelligence (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 275-290. Available from: https://doi.org/10.1007/978-3-031-47994-6_27

The use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behav... Read More about Explaining a staff rostering problem by mining trajectory variance structures..

Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. (2023)
Conference Proceeding
COLLINS, J., ZĂVOIANU, A.-C. and MCCALL, J.A.W. 2023. Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2023 (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 451-464. Available from: https://doi.org/10.1007/978-3-031-47994-6_39

Rosters are often used for real-world staff scheduling requirements. Multiple design factors such as demand variability, shift type placement, annual leave requirements, staff well-being and the placement of trainees need to be considered when constr... Read More about Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement..

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..

On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. (2022)
Conference Proceeding
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J. 2022. On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 104-111. Available from: https://doi.org/10.1007/978-3-031-25312-6_12

While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimise the benefits of adding connected and autonomous vehicles (CAVs) to existing multi-modal transport sy... Read More about On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems..

Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation. (2022)
Conference Proceeding
ZAVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2022. Lightweight Interpolation-based surrogate modelling for multiobjective continuous optimisation. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 53-60. Available from: https://doi.org/10.1007/978-3-031-25312-6_6

We propose two surrogate-based strategies for increasing the convergence speed of multi-objective evolutionary algorithms (MOEAs) by stimulating the creation of high-quality individuals early in the run. Both offspring generation strategies are desig... Read More about Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation..

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., ZAVOIANU, 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
ZAVOIANU, 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
ZAVOIANU, 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., ZAVOIANU, 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
ZAVOIANU, 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..

Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. (2019)
Conference Proceeding
ZAVOIANU, A.-C., SAMINGER-PLATZ, S. and AMRHEIN, W. 2019. Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, New Zealand. Piscataway: IEEE [online], article number 8790133, pages 3078-3085. Available from: https://doi.org/10.1109/CEC.2019.8790133

We describe and compare two steady state asynchronous parallelization variants for DECMO2++, a recently proposed multi-objective coevolutionary solver that generally displays a robust run-time convergence behavior. The two asynchronous variants were... Read More about Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver..

Potential identification and industrial evaluation of an integrated design automation workflow. (2019)
Journal Article
ENTNER, D., PRANTE, T., VOSGIEN, T., ZAVOIANU, A.-C., SAMINGER-PLATZ, S., SCHWARZ, M. and FINK, K. 2019. Potential identification and industrial evaluation of an integrated design automation workflow. Journal of engineering, design and technology [online], 17(6), pages 1085-1109. Available from: https://doi.org/10.1108/JEDT-06-2018-0096

Purpose - The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design sp... Read More about Potential identification and industrial evaluation of an integrated design automation workflow..

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models. (2019)
Journal Article
LUGHOFER, E., ZAVOIANU, A.-C., POLLAK, R., PRATAMA, M., MEYER-HEYE, P., ZÖRRER, H., EITZINGER, C. and RADAUER, T. 2019. Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models. Journal of process control [online], 76, pages 27-45. Available from: https://doi.org/10.1016/j.jprocont.2019.02.005

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potent... Read More about Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models..

Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm. (2018)
Conference Proceeding
ZAVOIANU, A.-C., SAMINGER-PLATZ, S., LUGHOFER, E. and AMRHEIN, W. 2018. Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm. In Aguirre, H. (ed.) Proceedings of the 2018 Genetic and evolutionary computation conference (GECCO'18), 15-19 July 2018, Kyoto, Japan. New York: Association for Computing Machinery [online], pages 793-800. Available from: https://doi.org/10.1145/3205455.3205549

We describe two enhancements that significantly improve the rapid convergence behavior of DECM02 - a previously proposed robust coevolutionary algorithm that integrates three different multi-objective space exploration paradigms: differential evoluti... Read More about Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm..

Multi-objective optimal design of obstacle-avoiding two-dimensional Steiner trees with application to ascent assembly engineering. (2018)
Journal Article
ZAVOIANU, A.-C., SAMINGER-PLATZ, S., ENTNER, D., PRANTE, T., HELLWIG, M., SCHWARZ, M. and FINK, K. 2018. Multi-objective optimal design of obstacle-avoiding two-dimensional Steiner trees with application to ascent assembly engineering. Journal of mechanical design [online], 140(6), article number 061401. Available from: https://doi.org/10.1115/1.4039009

We present an effective optimization strategy that is capable of discovering high-quality cost-optimal solution for two-dimensional (2D) path network layouts (i.e., groups of obstacle-avoiding Euclidean Steiner trees) that, among other applications,... Read More about Multi-objective optimal design of obstacle-avoiding two-dimensional Steiner trees with application to ascent assembly engineering..

Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems. (2015)
Journal Article
ZAVOIANU, A.-C., LUGHOFER, E., KOPPELSTÄTTER, W., WEIDENHOLZER, G., AMRHEIN, W. and KLEMENT, E.P. 2015. Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems. Knowledge-based systems [online], 87, pages 47-60. Available from: https://doi.org/10.1016/j.knosys.2015.05.029

In the last two decades, multi-objective evolutionary algorithms (MOEAs) have become ever more used in scientific and industrial decision support and decision making contexts the require an a posteriori articulation of preference. The present work is... Read More about Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems..