Abstract
In this paper, our main concern is to solve expensive robust optimization with moderate to high dimensionality of the decision variables under the constraint of limited computational budget. For this, we propose a local-surrogate based multi-objective memetic algorithm to solve the optimization problem coupled with uncertainty quantification method to calculate the robustness values. The robust optimization framework was applied to two aerodynamic cases to assess its capability on real world problems. Result on subsonic airfoil shows that the surrogate-based optimizer can produce non-dominated solutions with better quality than the non-surrogate optimizer. It also successfully solved the transonic airfoil optimization problem and found a strong tradeoff between the mean and standard deviation of lift-to-drag ratio.
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Part of this work is funded through Riset KK 2016 ITB.
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Palar, P.S., Dwianto, Y.B., Zuhal, L.R., Tsuchiya, T. (2016). Framework for Robust Optimization Combining Surrogate Model, Memetic Algorithm, and Uncertainty Quantification. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_5
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DOI: https://doi.org/10.1007/978-3-319-41000-5_5
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