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Acta Mechanica Sinica
Erschienen in: Acta Mechanica Sinica 2/2020

10.04.2020 | Research Paper

Hybrid algorithms for handling the numerical noise in topology optimization

verfasst von: Pooya Rostami, Javad Marzbanrad

Erschienen in: Acta Mechanica Sinica | Ausgabe 2/2020

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Abstract

This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching–learning based optimization (TLBO) and the method of moving asymptotes (MMA). The topology optimization problem is parameterizing with a low dimensional explicit method called moving morphable components (MMC), to make the use of evolutionary algorithms more efficient. Gradient-based solvers have good performance in solving large-scale topology optimization problems. However, in unconventional cases same as crashworthiness design in which there is numerical noise in the gradient information, the uses of these algorithms are unsuitable. The standard evolutionary algorithms can solve such problems since they don’t need gradient information. However, they have a high computational cost. This paper is based upon the idea of combining metaheuristics with mathematical programming to handle the probable noises and have faster convergence speed. Due to the ease of computations, the compliance minimization problem is considered as the case study and the artificial noise is added in gradient information.
Vorheriger Artikel Effect of interfacial delamination on coating crack in thick diamond-like carbon coatings under indentation

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Literatur
1.
Zhu, D., Zhan, W.: Topology optimization of a 6-DOF spatial compliant mechanism based on Stewart propotype platform. Acta. Mech. Sin. 35, 1044–1059 (2019)MathSciNet
2.
Bendsøe, M.P.: Topology optimization. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization. Springer, Boston, pp. 2636–2638 (2001)
3.
Sigmund, O.A.: 99-line topology optimization code written in Matlab. J. Struct. Multidiscip. Optim. 21, 120–127 (2001)
4.
Tavakoli, R.: Multimaterial topology optimization by volume constrained Allen–Cahn system and regularized projected steepest descent method. J. Comput. Methods Appl. Mech. Eng. 276, 534–565 (2014)MathSciNetMATH
5.
Huang, G., Chen, X., Yang, Z.: A modified gradient projection method for static and dynamic topology optimization. J. Eng. Optim. 50, 1515–1532 (2018)MathSciNet
6.
Svanberg, K.: The method of moving asymptotes—a new method for structural optimization. Int. J. Numer. Methods Eng. 24, 359–373 (1987)MathSciNetMATH
7.
Behrou, R., Guest, J.K.: Topology optimization for transient response of structures subjected to dynamic loads. In: 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p. 3657 (2017)
8.
Li, L., Khandelwal, K.: Two-point gradient-based MMA (TGMMA) algorithm for topology optimization. J. Comput. Struct. 131, 34–45 (2014)
9.
Huang, X., Xie, M.: Evolutionary Topology Optimization of Continuum Structures: Methods and Applications. Wiley, Hoboken (2010)MATH
10.
Aulig, N.: Generic Topology Optimization Based on Local State Features, vol. 468. VDI Verlag, Düsseldorf (2017)
11.
Gomes, A.A., Suleman, A.: Application of spectral level set methodology in topology optimization. J. Struct. Multidiscip. Optim. 31, 430–443 (2006)MathSciNetMATH
12.
Sigmund, O.: On the usefulness of non-gradient approaches in topology optimization. J. Struct. Multidiscip. Optim. 43, 589–596 (2011)MathSciNetMATH
13.
Kita, E., Tanie, H.: Topology and shape optimization of continuum structures using GA and BEM. J. Struct. Optim. 17, 130–139 (1999)
14.
Wang, N., Zhang, X.: Compliant mechanisms design based on pairs of curves. J. Sci. China Technol. Sci 55, 2099–2106 (2012)
15.
Wang, S.Y., Tai, K.: A bit-array representation GA for structural topology optimization. In: 2003 Congress on Evolutionary Computation, CEC’03, pp. 671–677 (2003)
16.
Tai, K., Chee, T.H.: Design of structures and compliant mechanisms by evolutionary optimization of morphological representations of topology. J. Mech. Des. 122, 560–566 (2000)
17.
Wang, S.Y., Tai, K., Wang, M.Y.: An enhanced genetic algorithm for structural topology optimization. Int. J. Numer. Methods Eng. 65, 18–44 (2006)MATH
18.
Zhou, H.: Topology optimization of compliant mechanisms using hybrid discretization model. J. Mech. Des. 132, 111003 (2010)
19.
Kaveh, A., Hassani, B., Shojaee, S., et al.: Structural topology optimization using ant colony methodology. Eng. Struct. 30, 2559–2565 (2008)
20.
Madeira, J.A., Rodrigues, H.C., Pina, H.: Multiobjective topology optimization of structures using genetic algorithms with chromosome repairing. J. Struct. Multidiscip. Optim. 32, 31–39 (2006)
21.
Padhye, N.: Topology optimization of compliant mechanism using multi-objective particle swarm optimization. In: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1831–1834 (2008)
22.
Kobayashi, M.H.: On a biologically inspired topology optimization method. J. Commun. Nonlinear Sci. Numer. Simul. 15, 787–802 (2010)MathSciNetMATH
23.
Sabbatini, E., Revel, G.M., Kobayashi, M.H.: Vibration reduction using biologically inspired topology optimization method: optimal stiffeners distribution on an acoustically excited plate. J. Vib. Control 21, 1398–1412 (2015)MathSciNet
24.
Wu, C.Y., Tseng, K.Y.: Topology optimization of structures using modified binary differential evolution. J. Struct. Multidiscip. Optim. 42, 939–953 (2010)
25.
Zhou, H., Ting, K.L.: Shape and size synthesis of compliant mechanisms using wide curve theory. J. Mech. Des. 128, 551–558 (2006)
26.
Guirguis, D., Aly, M.F.: A derivative-free level-set method for topology optimization. J. Finite Elem. Anal. Des. 120, 41–56 (2016)MathSciNet
27.
Luh, G.C., Chueh, C.H.: Multi-modal topological optimization of structure using immune algorithm. J. Comput. Methods Appl. Mech. Eng. 193, 4035–4055 (2004)MATH
28.
Campelo, F., Watanabe, K., Igarashi, H.: 3D topology optimization using an immune algorithm. COMPEL Int. J. Comput. Math. Electr. Electron. Eng. 26, 677–688 (2007)MATH
29.
Bureerat, S., Limtragool, J.: Performance enhancement of evolutionary search for structural topology optimisation. Finite Elem. Anal. Des. 42, 547–566 (2006)MathSciNet
30.
Bureerat, S., Limtragool, J.: Structural topology optimisation using simulated annealing with multiresolution design variables. Finite Elem. Anal. Des. 44, 738–747 (2008)
31.
Sleesongsom, S., Bureerat, S.: Topology optimisation using MPBILs and multi-grid ground element. J. Appl. Sci. 8, 271 (2018)
32.
Gomes, A.A., Suleman, A.: Topology optimization of a reinforced wing box for enhanced roll maneuvers. Aiaa J 46, 548–556 (2008)
33.
Aulig, N., Olhofer, M.: Evolutionary computation for topology optimization of mechanical structures: an overview of representations. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1948–1955 (2016)
34.
Bochenek, B., Tajs-Zieli´nska, K.: Novel local rules of cellular automata applied to topology and size optimization. J. Eng. Optim. 44, 23–35 (2012)
35.
Hunkeler, S.: Topology optimisation in crashworthiness design via hybrid cellular automata for thin walled structures, [Ph.D. Thesis], Queen Mary University of London (2014)
36.
Hunkeler, S., Duddeck, F., Rayamajhi, M.: Topology optimisation method for crashworthiness design using hybrid cellular automata and thin-walled ground structures. In: 9th Europ LS-DYNA Conf., Manchester (2013)
37.
Duddeck, F., Hunkeler, S., Lozano, P., et al.: Topology optimization for crashworthiness of thin-walled structures under axial impact using hybrid cellular automata. J. Struct. Multidiscip. Optim. 54, 415–428 (2016)
38.
Zeng, D., Duddeck, F.: Improved hybrid cellular automata for crashworthiness optimization of thin-walled structures. J. Struct. Multidiscip. Optim. 56, 101–115 (2017)MathSciNet
39.
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)MathSciNetMATH
40.
Allaire, G., Jouve, F., Toader, A.M.: Structural optimization using sensitivity analysis and a level-set method. J. Comput. Phys. 194, 363–393 (2004)MathSciNetMATH
41.
Wei, P., Li, Z., Li, X., et al.: An 88-line MATLAB code for the parameterized level set method based topology optimization using radial basis functions. J. Struct. Multidiscip. Optim. 58, 831–849 (2018)MathSciNet
42.
Yulin, M., Xiaoming, W.: A level set method for structural topology optimization with multi-constraints and multi-materials. J. Acta Mech. Sin. 20, 507–518 (2004)MathSciNet
43.
Zhang, W., Yang, W., Zhou, J., et al.: Structural topology optimization through explicit boundary evolution. J. Appl. Mech. 84, 011011 (2017)
44.
Zhang, W., Chen, J., Zhu, X., et al.: Explicit three dimensional topology optimization via moving morphable void (MMV) approach. J. Comput. Methods Appl. Mech. Eng. 322, 590–614 (2017)MathSciNetMATH
45.
Guo, X., Zhang, W., Zhong, W.: Doing topology optimization explicitly and geometrically—a new moving morphable components based framework. J. Appl. Mech. 81, 081009 (2014)
46.
Zhang, W., Zhang, J., Guo, X.: Lagrangian description based topology optimization—a revival of shape optimization. J. Appl. Mech. 83, 041010 (2016)
47.
Xue, R., Liu, C., Zhang, W., et al.: Explicit structural topology optimization under finite deformation via moving morphable void (MMV) approach. J. Comput. Methods Appl. Mech. Eng. 344, 798–818 (2019)MathSciNetMATH
48.
Zhang, W., Zhou, J., Zhu, Y., et al.: Structural complexity control in topology optimization via moving morphable component (MMC) approach. J. Struct. Multidiscip. Optim. 56, 535–552 (2017)MathSciNet
49.
Zhang, W., Li, D., Zhou, J., et al.: A moving morphable void (MMV)-based explicit approach for topology optimization considering stress constraints. J. Comput. Methods Appl. Mech. Eng. 334, 381–413 (2018)MathSciNetMATH
50.
Zhang, W., Song, J., Zhou, J., et al.: Topology optimization with multiple materials via moving morphable component (MMC) method. J. Comput. Methods Appl. Mech. Eng. 113, 1653–1675 (2018)MathSciNet
51.
Liu, C., Zhu, Y., Sun, Z., et al.: An efficient moving morphable component (MMC)-based approach for multi-resolution topology optimization. J. Struct. Multidiscip. Optim. 58, 2455–2479 (2018)MathSciNet
52.
Guo, X., Zhang, W., Zhang, J., et al.: Explicit structural topology optimization based on moving morphable components (MMC) with curved skeletons. J. Comput. Methods Appl. Mech. Eng. 310, 711–748 (2016)MathSciNetMATH
53.
Guo, X., Zhou, J., Zhang, W., et al.: Self-supporting structure design in additive manufacturing through explicit topology optimization. J. Comput. Methods Appl. Mech. Eng. 323, 27–63 (2017)MathSciNetMATH
54.
Liu, C., Du, Z., Zhang, W., et al.: Additive manufacturing-oriented design of graded lattice structures through explicit topology optimization. J. Appl. Mech. 84, 081008 (2017)
55.
Zhang, W., Li, D., Kang, P., et al.: Explicit topology optimization using IGA-based moving morphable void (MMV) approach. J. Comput. Methods Appl. Mech. Eng. 360, 112685 (2019)MathSciNetMATH
56.
Marzbanrad, J., Hoseinpour, A.: Structural optimization of MacPherson control arm under fatigue loading. J. Tehnički Vjesnik 24, 917–924 (2017)
57.
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. J. Comput.-Aided Des. 43, 303–315 (2011)
58.
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. J. Inf. Sci. 183, 1–15 (2012)MathSciNet
59.
Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of IEEE Evolutionary Computation International Conference, pp. 312–317 (1996)
60.
Bujny, M., Aulig, N., Olhofer, M., et al.: Identification of optimal topologies for crashworthiness with the evolutionary level set method. Int. J. Crashworthiness 23, 395–416 (2018)
61.
Bujny, M., Aulig, N., Olhofer, M., et al.: Hybrid evolutionary approach for level set topology optimization. In: Evolutionary Computation (CEC), 2016 IEEE Congress, pp. 5092–5099 (2016)
62.
Bujny, M., Aulig, N., Olhofer, M., et al.: Evolutionary level set method for crashworthiness topology optimization. In: ECCOMAS Congress, Hersonissos, Greece, pp. 31–42 (2016)
63.
Bujny, M., Aulig, N., Olhofer, M., et al.: Evolutionary crashworthiness topology optimization of thin-walled structures. In: 11th ASMO UK/ISSMO/NOED2016: International Conference on Numerical Optimisation Methods for Engineering Design. Munich, Germany, pp. 115–125 (2016)
64.
Beyer, H.G., Sendhoff, B.: Covariance matrix adaptation revisited—the CMSA evolution strategy. In: International Conference on Parallel Problem Solving from Nature, pp. 123–132 (2008)
65.
Bujny, M., Aulig, N., Olhofer, M., et al.: Learning-based topology variation in evolutionary level set topology optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 825–832 (2018)
66.
Raponi, E., Bujny, M., Olhofer, M., et al.: Kriging-guided level set method for crash topology optimization. In: 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry, Stuttgart, Germany, pp. 115–123 (2017)
67.
Raponi, E., Bujny, M., Olhofer, M., et al.: Kriging-assisted topology optimization of crash structures. J. Comput. Methods Appl. Mech. Eng. 348, 730–752 (2019)MathSciNetMATH
68.
Woo, H.W., Kwon, H.H., Tahk, M.J.: A hybrid method of evolutionary algorithms and gradient search. In: 2nd International Conference on Autonomous Robots and Agents, pp. 115–123 (2004)
69.
van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: van Laarhoven, P.J. (ed.) Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Dordrecht (1987)MATH
70.
Overvelde, J.T.: The moving node approach in topology optimization, [Master Thesis], Delft University (2012)
Metadaten
Titel
Hybrid algorithms for handling the numerical noise in topology optimization
verfasst von
Pooya Rostami
Javad Marzbanrad
Publikationsdatum
10.04.2020
Verlag
The Chinese Society of Theoretical and Applied Mechanics; Institute of Mechanics, Chinese Academy of Sciences
Erschienen in
Acta Mechanica Sinica / Ausgabe 2/2020
Print ISSN: 0567-7718
Elektronische ISSN: 1614-3116
DOI
https://doi.org/10.1007/s10409-020-00942-7

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