Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 2
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Material parameters identification by inverse analysis using finite element computations leads to the resolution of complex and time-consuming optimization problems. One way to deal with these complex problems is to use meta-models to limit the number of objective function computations. In this paper, the Efficient Global Optimization (EGO) algorithm is used. The EGO algorithm is applied to specific objective functions, which are representative of material parameters identification issues. Isotropic and anisotropic correlation functions are tested. For anisotropic correlation functions, it leads to a significant reduction of the computation time. Besides, they appear to be a good way to deal with the weak sensitivity of the parameters. In order to decrease the computation time, a parallel strategy is defined. It relies on a virtual enrichment of the meta-model, in order to compute q new objective functions in a parallel environment. Different methods of choosing the qnew objective functions are presented and compared. Speed-up tests show that Kriging Believer (KB) and minimum Constant Liar (CLmin) enrichments are suitable methods for this parallel EGO (EGO-p) algorithm. However, it must be noted that the most interesting speed-ups are observed for a small number of objective functions computed in parallel. Finally, the algorithm is successfully tested on a real parameters identification problem.

Go to article

Bibliography

[1] P.A. Prates, M.C Oliveira, and J.V. Fernandes. Identification of material parameters for thin sheets from single biaxial tensile test using a sequential inverse identification strategy. International Journal of Material Forming, 9:547–571, 2016. doi: 10.1007/s12289-015-1241-z.
[2] M. Gruber, N. Lebaal, S. Roth, N. Harb, P. Sterionow, and F. Peyraut. Parameter identification of hardening laws for bulk metal forming using experimental and numerical approach. International Journal of Material Forming, 9:21–33. doi: 10.1007/s12289-014-1196-5.
[3] R. Amaral, P. Teixeira, A.D. Santos, and J.C. de Sá. Assessment of different ductile damage models and experimental validation. International Journal of Material Forming, 11, 435–444, 2018. doi: 10.1007/s12289-017-1381-4.
[4] J. Nocedal and S. Wright. Numerical Optimization, 2nd ed. Springer-Verlag, New York, 2006.
[5] J.A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 1965. doi: 10.1093/comjnl/7.4.308.
[6] K.Y. Lee and F.F. Yang. Optimal reactive power planning using evolutionalry algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming. IEEE Transactions on Power Systems, 13(1):101–108, 1998. doi: 10.1109/59.651620.
[7] N. Stander, K.J. Craig, H. Müllerschön, and R. Reichert. Material identification in structural optimization using response surfaces. Structural and Multidisciplinary Optimization, 29:93–102, 2005. doi: 10.1007/s00158-004-0476-y.
[8] M. Ageno, G. Bolzon, and G. Maier. An inverse analysis procedure for the material parameter identification of elastic- plastic free-standing foils. Structural and Multidisciplinary Optimization, 38:229–243, 2009. doi: 10.1007/s00158-008-0294-8.
[9] M. Abendroth and M. Kuna. Identification of ductile damage and fracture parameters from the small punch test using neural networks. Engineering Fracture Mechanics, 73(6):710–725, 2006. doi: 10.1016/j.engfracmech.2005.10.007.
[10] R. Franchi, A. Del Prete, and D. Umbrell. Inverse analysis procedure to determine flow stress and friction data for finite element modeling of machining. International Journal of Material Forming, 10:685–695, 2017. doi: 10.1007/s12289-016-1311-x.
[11] N. Souto, A. Andrade-Campos, and S. Thuillier. Mechanical design of a heterogeneous test for material parameters identification. International Journal of Material Forming, 10:353–367, 2017. doi: 10.1007/s12289-016-1284-9.
[12] M. Rackl, K.J. Hanley, and W.A. Günthner. Verification of an automated work flow for discrete element material parameter calibration. In: Li X., Feng Y., Mustoe G. (eds.), Proceedings of the 7th International Conference on Discrete Element Methods. DEM 2016, volume 188, pages 201–208. Springer, Singapore 2017. doi: 10.1007/978-981-10-1926-5_23.
[13] K. Levenberg. A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2(2):164–168, 1944.
[14] C. Richter, T. Rößler, G. Kunze, A. Katterfeld, and F. Will. Development of a standard calibration procedure for the DEM parameters of cohesionless bulk materials – Part II: Efficient optimization-based calibration. Powder Technology, 360:967–976, 2020. doi: 10.1016/j.powtec.2019.10.052.
[15] D.R. Jones, M. Schonlau, and W.J. Welch. Efficient global optimization of expensive black-box function. Journal of Global Optimization, 13:455–492, 1998. doi: 10.1023/A:1008306431147.
[16] M.T.M. Emmerich, K.C. Giannakoglou, and B. Naujoks. Single- and multi-objective evolutionary optimization assisted by gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4):421–439, 2006. doi: 10.1109/TEVC.2005.859463.
[17] T.J. Santner, B.J. Williams, and W.I. Notz. The Design and Analysis of Computer Experiments. Springer, New York, 2018. doi: 10.1007/978-1-4939-8847-1.
[18] E. Brochu, V.M. Cora, and N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical Report TR-2009-23, Department of Computer Science, University of British Columbia, Canada, November 2009.
[19] D. Ginsbourger, R. Riche, and L. Carraro. Kriging is well-suited to parallelize optimization. In Y. Tenne, Ch-K. Goh (eds.), Computational Intelligence in Expensive Optimization Problems, volume 2, pages 131–162, Springer-Verlag, Berlin, 2010.
[20] E. Roux and P.-O. Bouchard. Kriging metamodel global optimization of clinching joining processes accounting for ductile damage. Journal of Materials Processing Technology, 213(7):1038–1047, 2013. doi: 10.1016/j.jmatprotec.2013.01.018.
[21] E. Roux and P.-O. Bouchard. On the interest of using full field measurements in ductile damage model calibration. International Journal of Solids and Structures, 72:50–62, 2015. doi: 10.1016/j.ijsolstr.2015.07.011.
[22] J. Sacks, W.J. Welch, T.J. Mitchell, and H.P. Wynn. Design and analysis of computer experiments. Statistical Science, 4(4):409–423, 1989.
[23] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer M. et al. (eds,), Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, volume 1917, pages 849–858. Springer, Berlin, Heidelberg, 2000. doi: 10.1007/3-540-45356-3_83.
[24] M. Hamdaoui, F.Z. Oujebbour, A. Habbal, P. Breitkopf, and P. Villon. Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications. International Journal of Material Forming, 8:469–480, 2015. doi: 10.1007/s12289-014-1190-y.
[25] J.J. Droesbeke, M. Lejeune, and G. Saporta. Statistical Analysis of Spatial Data. Editions TECHNIP, 1997 (in French).
[26] C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
[27] J.D. Martin and T.W. Simpson. Use of kriging models to approximate deterministic computer models. AIAA Journal, 43(4):853–863, 2005. doi: 10.2514/1.8650.
[28] J. Laurenceau and P. Sagaut. Building efficient response surface of aerodynamic function with kriging and cokriging. AIAA Journal, 46(2):498–507, 2008. doi: 10.2514/1.32308.
[29] D.R. Jones. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21:345–383, 2001. doi: 10.1023/A:1012771025575.
[30] V.A. Dirk and H.G. Beyer. A comparison of evolution strategies with other direct search methods in the presence of noise. Computational Optimization and Applications, 24:135–159, 2003. doi: 10.1023/A:1021810301763.
[31] J.A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 1965. doi: 10.1093/comjnl/7.4.308.
[32] P.-O. Bouchard, J.-M. Gachet, and E. Roux. Ductile damage parameters identification for cold metal forming applications. AIP Conference Proceedings, 1353(1):47–52, 2011. doi: 10.1063/1.3589490.
[33] D. Huang, T.T. Allan, W.I. Notz, and N. Zeng. Global optimization of stochastic black-box systems via sequential kriging meta-models. Journal of Global Optimization, 34:441–466, 2006. doi: 10.1007/s10898-005-2454-3.
[34] H.H. Rosenbrock. An automatic method for finding the greatest or least value of a function. The Computer Journal, 3(3):175–184, 1960. doi: 10.1093/comjnl/3.3.175.
[35] M. Pillet. The Taguchi Method Experiment Plans. Les Edition d'Organisation, 2005 (in French).
[36] H. Digonnet, L. Silva, and T. Coupez. Cimlib: A Fully Parallel Application For Numerical Simulations Based On Components Assembly. AIP Conference Proceedings, 908:269–274, 2007. doi: 10.1063/1.2740823.
[37] P.-O. Bouchard, L. Bourgeon, S. Fayolle, and K. Mocellin. An enhanced Lemaitre model formulation for materials processing damage computation. International Journal of Material Forming, 4:299–315, 2011. doi: 10.1007/s12289-010-0996-5.
[38] E. Roux, M. Thonnerieux, and P.-O. Bouchard. Ductile damage material parameter identification: numerical investigation. In Proceedings of the Tenth International Conference on Computational Structures Technology, paper 135, Civil-Comp Press, 2010. doi: 10.4203/ccp.93.135.
Go to article

Authors and Affiliations

Emile Roux
1
Yannick Tillier
2
Salim Kraria
2
Pierre-Olivier Bouchard
2

  1. Université Savoie Mont-Blanc, SYMME, F-74000 Annecy, France.
  2. MINES ParisTech, PSL Research University, CEMEF-Centre de mise en forme des matériaux, CNRS UMR 7635, CS 10207 rue Claude Daunesse, 06904 Sophia Antipolis Cedex, France
Download PDF Download RIS Download Bibtex

Abstract

In this paper,we proposed a modified meta-heuristic algorithm based on the blind naked mole-rat (BNMR) algorithm to solve the multiple standard benchmark problems. We then apply the proposed algorithm to solve an engineering inverse problem in the electromagnetic field to validate the results. The main objective is to modify the BNMR algorithm by employing two different types of distribution processes to improve the search strategy. Furthermore, we proposed an improvement scheme for the objective function and we have changed some parameters in the implementation of the BNMR algorithm. The performance of the BNMR algorithm was improved by introducing several new parameters to find the better target resources in the implementation of a modified BNMR algorithm. The results demonstrate that the changed candidate solutions fall into the neighborhood of the real solution. The results show the superiority of the propose method over other methods in solving various mathematical and electromagnetic problems.
Go to article

Bibliography

[1] Taherdangkoo M., Modified stem cells algorithm for Loney’s solenoid benchmark problem, International Journal Applied Electromagnetics and Mechanics, vol. 42, no. 3, pp. 437–445 (2013).
[2] Coelho L.D.S., Alotto P., Loney’s Solenoid Design Using an Artificial Immune Network with Local Search Based on the Simplex Method, IEEE Transactions on Magnetics, vol. 44, no. 6, pp. 1070–1073 (2008).
[3] Khan T.A., Sai Ho Ling, An improved gravitational search algorithm for solving an electromagnetic design problem, Journal of Computational Electronics, vol. 19, no. 2, pp. 773–779 (2020), DOI: 10.1007/s10825-020-01476-8.
[4] Duca A., Ciuprina G., Lup S., Hameed I., ACORalgorithm’s efficiency for electromagnetic optimization benchmark problems, International Symposium on Advanced Topics in Electrical Engineering, pp. 1–5 (2019).
[5] Coelho L.D.S., Alotto P., Gaussian Artificial Bee Colony Algorithm Approach Applied to Loney’s Solenoid Benchmark Problem, IEEE Transactions on Magnetics, vol. 47, no. 5, pp. 1326–1329 (2011).
[6] Duca A., Duca L., Ciuprina G., QPSO with avoidance behavior to solve electromagnetic optimization problems, International Journal of Applied Electromagnetics and Mechanics, vol. 1, pp. 1–7 (2018).
[7] Coelho L.D.S., Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems, Expert Systems with Applications, vol. 37, pp. 1676–1683 (2010).
[8] Ciuprina G., Ioan D., Munteanu I., Use of Intelligent-particle swarm optimization in electromagnetic, IEEE Transactions on Magnetics, vol. 38, no. 2, pp. 1037–1040 (2002).
[9] Rehman O., Yang Sh., Khan Sh., Rahman S., A quantum particle swarm optimizer with enhanced strategy for global optimization of electromagnetic devices, IEEE Transactions on Magnetics, vol. 55, no. 8 (2019), DOI: 10.1109/TMAG.2019.2913021.
[10] Taherdangkoo M., Shirzadi M.H., Yazdi M., Bagheri M.H., A robust clustering method based on blind, naked mole-rats (BNMR) algorithm, Swarm and Evolutionary Computation, vol. 10, pp. 1–11 (2013).
[11] Taherdangkoo M., Taherdangkoo M., Modified BNMR algorithm applied to Loney’s solenoid benchmark problem, International Journal of Applied Electromagnetics and Mechanics, vol. 46, no. 3, pp. 683–692 (2014).
[12] Taherdangkoo M., Shirzadi M.H., Bagheri M.H., A novel meta-heuristic algorithm for numerical function optimization: Blind, naked mole-rats (BNMR) algorithm, Scientific Research and Essays, vol. 7, no. 41, pp. 3566–3583 (2012).
[13] Suganthan P.N., Hansen N., Liang J.J., Deb K., Chen Y.P., Auger A., Tiwari S., Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization, Kanpur Genetic Algorithms Lab., IIT Kanpur, Nanyang Technol. Univ., Singapore, KanGAL Rep. 2005005 (2005).
[14] Di Barba G., Savini A., Global optimization of Loney’s solenoid: a benchmark problem, International Journal of Applied Electromagnetics and Mechanics, vol. 6, no. 4, pp. 273–276 (1995).
[15] Klein C.E., Segundo E.H.V., Mariani V.C., Coelho L.D.S., Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization, IEEE Transactions on Magnetics, vol. 52, no. 3 (2015), DOI: 10.1109/TMAG.2015.2483059.
[16] Ye X., Wang P., Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problems, Applied Soft Computing, vol. 85 (2019), DOI: 10.1016/j.asoc.2019.105800.
Go to article

Authors and Affiliations

Mohammad Taherdangkoo
1
ORCID: ORCID

  1. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

This page uses 'cookies'. Learn more