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Single Screw Extrusion Optimization Using the Tchebycheff Scalarization Method | SpringerLink
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Single Screw Extrusion Optimization Using the Tchebycheff Scalarization Method

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Abstract

The optimal design of a single screw extrusion (SSE) is a very difficult task since it deals with several conflicting performance indices. Past research to find the optimal SSE design has been successfully conducted by optimization procedures, in particular by multi-objective optimization. Problems with two or more objectives have been addressed by multi-objective evolutionary algorithms that search for the whole set of promising solutions in a single run. Our approach has been guided by the bi-objective optimization problems, using a methodology based on the weighted Tchebycheff scalarization function. The numerical results show that the proposed methodology is able to produce satisfactory results with physical meaning.

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References

  1. Covas, J.A., Gaspar-Cunha, A., Oliveira, P.: An optimization approach to practical problems in plasticating single screw extrusion. Polym. Eng. Sci. 39, 443–456 (1999)

    Google Scholar 

  2. Covas, J.A., Gaspar-Cunha, A.: Optimisation-based design of extruders. Plast. Rubber Compos. 33(9–10), 416–425 (2004)

    Google Scholar 

  3. Gaspar-Cunha, A., Covas, J.A.: RPSGAe - reduced pareto set genetic algorithm: application to polymer extrusion. In: Gandibleux, X., Sevaux, M., Sörensen, K., Tkindt, V. (eds.) Metaheuristics for Multiobjective Optimisation. Lecture Notes in Economics and Mathematical Systems, vol. 535, pp. 221–249. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-642-17144-4_9

    Chapter  Google Scholar 

  4. Covas, J.A., Gaspar-Cunha, A.: Polymer extrusion - setting the operating conditions and defining the screw geometry. In: Gaspar-Cunha, A., Covas, J.A. (eds.) Optimization in Polymer Processing, pp. 87–113. Nova Science Publishers (2011)

    Google Scholar 

  5. Gaspar-Cunha, A., Covas, J.A., Costa, M.F.P., Costa, L.: Optimization of single screw extrusion. In: Sikora, J.W., Dulebová, L. (eds.) Technological and Design Aspects of the Processing of Composites and Nanocomposites, vol. I, Scientific-Practical International Workshop (NewEX H2020-MSCA-RISE-2017) Technical University of Košice (2018)

    Google Scholar 

  6. Miettinen, K.M.: A posteriori methods. In: Nonlinear Multiobjective Optimization. International Series in Operations Research & Management Science, vol. 12, pp. 77–113. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5563-6_4

  7. Emmerich, M.T.M., Deutz, A.H.: A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat. Comput. 17(3), 585–609 (2018). https://doi.org/10.1007/s11047-018-9685-y

    Article  MathSciNet  Google Scholar 

  8. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)

    Google Scholar 

  9. Coello, C.A.C., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the Congress on Evolutionary Computation (CEC 2002) pp. 1051–1056 (2002)

    Google Scholar 

  10. Santana-Quintero, L.V., Coello, C.A.C.: An algorithm based on differential evolution for multi-objective problems. Int. J. Comput. Intell. Res. 1(2), 151–169 (2005)

    MathSciNet  Google Scholar 

  11. Angelo, J.S., Barbosa, H.J.C.: On ant colony optimization algorithms for multiobjective problems. In: Ostfeld, A. (ed.) Ant Colony Optimization - Methods and Application, InTech Europe, pp. 53–74 (2011)

    Google Scholar 

  12. Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)

    Google Scholar 

  13. Molina, J., Laguna, M., Marti, R., Caballero, R.: SSPMO: a scatter tabu search procedure for non-linear multiobjective optimization. INFORMS J. Comput. 19(1), 91–100 (2007)

    MathSciNet  MATH  Google Scholar 

  14. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Google Scholar 

  15. Zhang, Q., Liu, W., Tsang, E., Virginas, B.: Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans. Evol. Comput. 14(3), 456–474 (2010)

    Google Scholar 

  16. Feng, Z., Zhang, Q., Zhang, Q., Tang, Q., Yang, T., Ma, Y.: A multiobjective optimization based framework to balance the global and local exploitation in expensive optimization. J. Glob. Optim. 61, 677–694 (2015)

    MathSciNet  MATH  Google Scholar 

  17. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report 103, Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH), Zurich ETH Zentrum, Zurich (2001)

    Google Scholar 

  18. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)

    MATH  Google Scholar 

  19. Steuer, R.E., Choo, E.U.: An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Program. 26, 326–344 (1983)

    MathSciNet  MATH  Google Scholar 

  20. Deb, K., Miettinen, K., Chaudhuri, S.: Toward an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Trans. Evol. Comput. 14(6), 821–841 (2010)

    Google Scholar 

  21. Gogna, A., Tayal, A.: Metaheuristics: review and application. J. Exp. Theor. Artif. Intell. 25(4), 503–526 (2013)

    Google Scholar 

  22. Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    MathSciNet  MATH  Google Scholar 

  23. Ingber, L.: Adaptive simulated annealing (ASA): lessons learned. Control Cybern. 25(1), 33–54 (1996)

    MATH  Google Scholar 

  24. Wah, B.W., Chen, Y., Wang, T.: Simulated annealing with asymptotic convergence for nonlinear constrained optimization. J. Glob. Optim. 39, 1–37 (2007)

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

The authors wish to thank two anonymous referees for their comments and suggestions to improve the paper.

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/05256/2020 and UIDP/05256/2020, UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM, and the European project MSCA-RISE-2015, NEWEX, Reference 734205.

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Correspondence to Ana Maria A. C. Rocha .

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Rocha, A.M.A.C., Matos, M.A., Costa, M.F.P., Gaspar-Cunha, A., Fernandes, E.M.G.P. (2020). Single Screw Extrusion Optimization Using the Tchebycheff Scalarization Method. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_48

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  • DOI: https://doi.org/10.1007/978-3-030-58808-3_48

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