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Framework for robust optimization combining surrogate model, memetic algorithm, and uncertainty quantification
Palar P.S.a, Dwianto Y.B.a, Zuhal L.R.a, Tsuchiya T.b
a Bandung Institute of Technology, Bandung, Indonesia
b University of Tokyo, Tokyo, 113-8656, Japan
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Springer International Publishing Switzerland 2016.In this paper, our main concern is to solve expensive robust optimization with moderate to high dimensionality of the decision vari-ables 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 quantifica-tion method to calculate the robustness values. The robust optimization framework was applied to two aerodynamic cases to assess its capabil-ity 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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Computational budget,Mean and standard deviations,Memetic algorithms,Nondominated solutions,Optimization problems,Robust optimization,Surrogate model,Uncertainty quantifications[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Multi-objective memetic algorithm,Robust optimization,Surrogate model,Uncertainty quantification[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Part of this work is funded through Riset KK 2016 ITB.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1007/978-3-319-41000-5_5[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]