All Outputs (9)

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], 25(9), pages 10790-10801. 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..

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..

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..

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..

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..

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..

DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm. (2014)
Journal Article
ZAVOIANU, A.-C., LUGHOFER, E., BRAMERDORFER, G., AMRHEIN, W. and KLEMENT, E.P. 2015. DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm. Soft computing [online], 19(12), pages 3551-3569. Available from: https://doi.org/10.1007/s00500-014-1308-7

We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes of multi-objective evolu... Read More about DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm..