Dr Ciprian Zavoianu's Outputs (25)

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)
Presentation / Conference Contribution
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..

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..