Interactive exploration of design trade-offs

Typical design for manufacturing applications requires simultaneous optimization of conflicting performance objectives: Design variations that improve one performance metric may decrease another performance metric. In these scenarios, there is no unique optimal design but rather a set of designs that are optimal for different trade-offs (called Pareto-optimal). In this work, we propose a novel approach to discover the Pareto front, allowing designers to navigate the landscape of compromises efficiently. Our approach is based on a first-order approximation of the Pareto front, which allows entire neighborhoods rather than individual points on the Pareto front to be captured. In addition to allowing for efficient discovery of the Pareto front and the corresponding mapping to the design space, this approach allows us to represent the entire trade-off manifold as a small collection of patches that comprise a high-quality and piecewise-smooth approximation. We illustrate how this technique can be used for navigating performance trade-offs in computer-aided design (CAD) models.

[1]  A. Messac,et al.  The normalized normal constraint method for generating the Pareto frontier , 2003 .

[2]  Sylvain Lefebvre,et al.  Make it stand , 2013, ACM Trans. Graph..

[3]  Takeo Igarashi,et al.  Pteromys: interactive design and optimization of free-formed free-flight model airplanes , 2014, ACM Trans. Graph..

[4]  Frédo Durand,et al.  Structural optimization of 3D masonry buildings , 2012, ACM Trans. Graph..

[5]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[6]  Tao Ju,et al.  Mean value coordinates for closed triangular meshes , 2005, ACM Trans. Graph..

[7]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[8]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Baining Guo,et al.  Fabricating spatially-varying subsurface scattering , 2010, ACM Trans. Graph..

[10]  Eitan Grinspun,et al.  Sim-X: parallel system software for interactive multi-experiment computational studies , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[11]  Wojciech Matusik,et al.  Retrieval on parametric shape collections , 2017, TOGS.

[12]  M. Otaduy,et al.  Design and fabrication of materials with desired deformation behavior , 2010, ACM Trans. Graph..

[13]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[14]  Michiel van de Panne,et al.  Pareto Optimal Control for Natural and Supernatural Motions , 2013, MIG '13.

[15]  VekslerOlga,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001 .

[16]  Michael Wimmer,et al.  Reduced-order shape optimization using offset surfaces , 2015, ACM Trans. Graph..

[17]  Wojciech Matusik,et al.  Computational multicopter design , 2016, ACM Trans. Graph..

[18]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  Markus H. Gross,et al.  Interactive design of 3D-printable robotic creatures , 2015, ACM Trans. Graph..

[20]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[21]  C. Hillermeier Nonlinear Multiobjective Optimization: A Generalized Homotopy Approach , 2001 .

[22]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[23]  A. Shamsai,et al.  Multi-objective Optimization , 2017, Encyclopedia of Machine Learning and Data Mining.

[24]  Kalyanmoy Deb,et al.  Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization , 2015, EMO.

[25]  Wojciech Matusik,et al.  Retrieval on Parametric Shape Collections , 2017, ACM Trans. Graph..

[26]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[27]  Jiawei Zhang,et al.  A Survey of Multiobjective Evolutionary Algorithms , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[28]  Wojciech Matusik,et al.  Fab forms , 2015, ACM Trans. Graph..

[29]  Wojciech Matusik,et al.  Computational design of metallophone contact sounds , 2015, ACM Trans. Graph..

[30]  Bernhard Thomaszewski,et al.  LinkEdit: interactive linkage editing using symbolic kinematics , 2015, ACM Trans. Graph..

[31]  Takeo Igarashi,et al.  Guided exploration of physically valid shapes for furniture design , 2012, ACM Trans. Graph..

[32]  Wojciech Matusik,et al.  Two-Scale Topology Optimization with Microstructures , 2017, TOGS.