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15 results on '"Dwianto, Yohanes Bimo"'

5. On the Advantages of Searching Infeasible Regions in Constrained Evolutionary-Based Multi-Objective Engineering Optimization.

Author

Dwianto, Yohanes Bimo, Palar, Pramudita Satria, Zuhal, Lavi Rizki, and Oyama, Akira

Subjects
*EVOLUTIONARY algorithms, *ENGINEERING, *PROBLEM solving, *ENGINEERING design
Abstract

Solving a multiple-criteria optimization problem with severe constraints remains a significant issue in multi-objective evolutionary algorithms. The problem primarily stems from the need for a suitable constraint handling technique. One potential approach is balancing the search in feasible and infeasible regions to find the Pareto front efficiently. The justification for such a strategy is that the infeasible region also provides valuable information, especially in problems with a small percentage of feasibility areas. To that end, this paper investigates the potential of the infeasibility-driven principle based on multiple constraint ranking-based techniques to solve a multi-objective problem with a small feasibility ratio. By analyzing the results from intensive experiments on a set of test problems, including the realistic multi-objective car structure design and actuator design problem, it is shown that there is a significant improvement gained in terms of convergence by utilizing the generalized version of the multiple constraint ranking techniques. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Framework for Robust Optimization Combining Surrogate Model, Memetic Algorithm, and Uncertainty Quantification

Author

Palar, Pramudita Satria, Dwianto, Yohanes Bimo, Zuhal, Lavi Rizki, Tsuchiya, Takeshi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, Shi, Yuhui, editor, and Niu, Ben, editor

Published
2016
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9. Shapley Additive Explanations for Knowledge Discovery via Surrogate Models

Author

Palar, Pramudita Satria, Zuhal, Lavi Rizki, Dwianto, Yohanes Bimo, Shimoyama, Koji, Morlier, Joseph, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole nationale supérieure des Mines d'Albi-Carmaux - IMT Mines Albi (FRANCE), Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Institut Teknologi Bandung - ITB (INDONESIA), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Tohoku University (JAPAN), Institut Clément Ader - ICA (Toulouse, France), Institut Teknologi Bandung (ITB), Tohoku University [Sendai], Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Autre, Re-entry trajectory, Wing design, Shapley Additive Explanations, Knowledge Discovery, Surrogate Models
Abstract

International audience; It is sometimes desirable to delve further into how the inputs affect the output in design optimization and uncertainty analysis. Surrogate models such as Gaussian Process Regression and support vector regression are useful for such tasks and can be further enhanced by introducing advanced post-processing methods. This paper investigates Shapley Additive Explanation (SHAP) as a tool to aid surrogate-assisted data-driven analysis. In particular, surrogate-enabled SHAP analysis allows visualization of the input-output relationship in a meaningful way using summary SHAP plots and SHAP dependence plots. Some importantinformation that can be extracted and visualized from SHAP includes the importance of input variables (i.e., global sensitivity analysis), nonlinearity level, and level of interactions. The benefits of Shapley values for engineering analysis using surrogate models are demonstrated in three engineering test problems.

Published
2023

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