Using guiding heuristics to improve the dynamic checking of temporal properties in data dominated high-level designs

Dias, Alair; Silva, Diogenes C. da

doi:10.1109/isvlsi.2013.6654617

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…SASS was chosen because it has only one control parameter, which is the number of particles. This eliminates the need for fine-tuning control parameters before the algorithm can successfully applied to industrial problems (Nolle 2007;Dias Junior and da Silva Junior, 2013). Hence, the experiments would not be influenced by the meta-optimisation problem of tuning control parameters.…”

Section: Methodsmentioning

confidence: 99%

Self-Adaptive Stepsize Search Applied To Robust Engineering Design Optimisation

Krause¹,

Nolle²,

Cant³

2015

ECMS 2015 Proceedings Edited By: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova

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In engineering it is usually necessary to design systems as cheap as possible whilst ensuring that certain constraints are satisfied. Computational optimisation methods can help to find optimal designs. However, it is demonstrated in this work that an optimal design is often not robust against variations caused by the manufacturing process, which would result in unsatisfactory product quality. In order to avoid this, a novel meta-method is introduced, which can guide arbitrary optimisation algorithms towards more robust solutions. This was demonstrated on a standard benchmark problem, the pressure vessel design problem, for which a robust design was found using the proposed method together with self-adaptive stepsize search, an optimisation algorithm with only one control parameter to tune. The drop-out rate of a simulated manufacturing process was reduced by 30% whilst maintaining near-minimal production costs, demonstrating the potential of the proposed method.

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Section: Methodsmentioning

confidence: 99%