Abstract
We describe an effective optimization strategy that is capable of discovering innovative cost-optimal designs of complete ascent assembly structures. Our approach relies on a continuous 2D model abstraction, an application-inspired multi-objective formulation of the optimal design task and an efficient coevolutionary solver. The obtained results provide empirical support that our novel strategy is able to deliver competitive results for the underlying general optimization challenge: the (obstacle-avoiding) Euclidean Steiner Tree Problem.
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Acknowledgements
This work was supported by the K-Project “Advanced Engineering Design Automation” (AEDA) that is financed under the COMET funding scheme of the Austrian Research Promotion Agency.
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Zăvoianu, AC. et al. (2019). On the Optimization of 2D Path Network Layouts in Engineering Designs via Evolutionary Computation Techniques. In: Andrés-Pérez, E., González, L., Periaux, J., Gauger, N., Quagliarella, D., Giannakoglou, K. (eds) Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-89890-2_20
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