IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v319y2022i2d10.1007_s10479-021-04232-8.html
   My bibliography  Save this article

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

Author

Listed:
  • Luciano Ferreira Cruz

    (Juscelino Kubitschek de Oliveira, 2600
    Pontifical Catholic University of Parana)

  • Flavia Bernardo Pinto

    (Pontifical Catholic University of Parana)

  • Lucas Camilotti

    (Pontifical Catholic University of Parana)

  • Angelo Marcio Oliveira Santanna

    (Federal University of Bahia)

  • Roberto Zanetti Freire

    (Pontifical Catholic University of Parana)

  • Leandro Santos Coelho

    (Pontifical Catholic University of Parana
    Federal University of Paraná)

Abstract

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with non-dominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance. To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

Suggested Citation

  • Luciano Ferreira Cruz & Flavia Bernardo Pinto & Lucas Camilotti & Angelo Marcio Oliveira Santanna & Roberto Zanetti Freire & Leandro Santos Coelho, 2022. "Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process," Annals of Operations Research, Springer, vol. 319(2), pages 1565-1587, December.
    • Handle: RePEc:spr:annopr:v:319:y:2022:i:2:d:10.1007_s10479-021-04232-8
    DOI: 10.1007/s10479-021-04232-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04232-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-021-04232-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    2. Giorgio Consigli & Darinka Dentcheva & Francesca Maggioni, 2020. "Stochastic optimization: theory and applications," Annals of Operations Research, Springer, vol. 292(2), pages 575-580, September.
    3. Racha El-Hajj & Rym Nesrine Guibadj & Aziz Moukrim & Mehdi Serairi, 2020. "A PSO based algorithm with an efficient optimal split procedure for the multiperiod vehicle routing problem with profit," Annals of Operations Research, Springer, vol. 291(1), pages 281-316, August.
    4. Agoston E. Eiben & Jim Smith, 2015. "From evolutionary computation to the evolution of things," Nature, Nature, vol. 521(7553), pages 476-482, May.
    5. Shyong Shyu & Peng-Yeng Yin & Bertrand Lin, 2004. "An Ant Colony Optimization Algorithm for the Minimum Weight Vertex Cover Problem," Annals of Operations Research, Springer, vol. 131(1), pages 283-304, October.
    6. J. Tervo & P. Kolmonen & T. Lyyra-Laitinen & J.D. Pintér & T. Lahtinen, 2003. "An Optimization-Based Approach to the Multiple Static Delivery Technique in Radiation Therapy," Annals of Operations Research, Springer, vol. 119(1), pages 205-227, March.
    7. Murilo Wohlgemuth & Carlos Ernani Fries & Ângelo Márcio Oliveira Sant’Anna & Ricardo Giglio & Diego Castro Fettermann, 2020. "Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression," Annals of Operations Research, Springer, vol. 286(1), pages 703-717, March.
    8. Ehrgott, Matthias & Holder, Allen & Nohadani, Omid, 2018. "Uncertain Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 268(1), pages 231-242.
    9. Sahar Validi & Arijit Bhattacharya & P. J. Byrne, 2020. "Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model," Annals of Operations Research, Springer, vol. 290(1), pages 191-222, July.
    10. Site Wang & Harsha Gangammanavar & Sandra Ekşioğlu & Scott J. Mason, 2020. "Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization," Annals of Operations Research, Springer, vol. 292(1), pages 371-397, September.
    11. Giorgio Consigli & Darinka Dentcheva & Francesca Maggioni, 2020. "Correction to: Preface: Stochastic optimization: theory and applications," Annals of Operations Research, Springer, vol. 292(2), pages 1001-1001, September.
    12. Fouad Ben Abdelaziz & Houda Alaya & Prasanta Kumar Dey, 2020. "A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises," Annals of Operations Research, Springer, vol. 293(2), pages 557-586, October.
    13. Margaret M. Wiecek & Matthias Ehrgott & Alexander Engau, 2016. "Continuous Multiobjective Programming," International Series in Operations Research & Management Science, in: Salvatore Greco & Matthias Ehrgott & José Rui Figueira (ed.), Multiple Criteria Decision Analysis, edition 2, chapter 0, pages 739-815, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. F. F. Almeida & S. V. Conceição & L. R. Pinto & B. R. P. Oliveira & L. F. Rodrigues, 2022. "Optimal sales and operations planning for integrated steel industries," Annals of Operations Research, Springer, vol. 315(2), pages 773-790, August.
    2. Ghazale Kordi & Parsa Hasanzadeh-Moghimi & Mohammad Mahdi Paydar & Ebrahim Asadi-Gangraj, 2023. "A multi-objective location-routing model for dental waste considering environmental factors," Annals of Operations Research, Springer, vol. 328(1), pages 755-792, September.
    3. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    4. Wanting Zhang & Ming Zeng & Peng Guo & Kun Wen, 2022. "Variable Neighborhood Search for Multi-Cycle Medical Waste Recycling Vehicle Routing Problem with Time Windows," IJERPH, MDPI, vol. 19(19), pages 1-25, October.
    5. Atakan, Semih & Gangammanavar, Harsha & Sen, Suvrajeet, 2022. "Towards a sustainable power grid: Stochastic hierarchical planning for high renewable integration," European Journal of Operational Research, Elsevier, vol. 302(1), pages 381-391.
    6. Nayak, Dhyaan Sandeep & Misra, Shamik, 2024. "An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty," Energy, Elsevier, vol. 290(C).
    7. Manuel Chica & Joaquín Bautista & Jesica de Armas, 2019. "Benefits of robust multiobjective optimization for flexible automotive assembly line balancing," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 75-103, March.
    8. Luzhi Wang & Shuli Hu & Mingyang Li & Junping Zhou, 2019. "An Exact Algorithm for Minimum Vertex Cover Problem," Mathematics, MDPI, vol. 7(7), pages 1-8, July.
    9. Engau, Alexander & Sigler, Devon, 2020. "Pareto solutions in multicriteria optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 281(2), pages 357-368.
    10. Seyyed Amir Babak Rasmi & Ali Fattahi & Metin Türkay, 2021. "SASS: slicing with adaptive steps search method for finding the non-dominated points of tri-objective mixed-integer linear programming problems," Annals of Operations Research, Springer, vol. 296(1), pages 841-876, January.
    11. Pedro Pinacho-Davidson & Christian Blum, 2020. "Barrakuda : A Hybrid Evolutionary Algorithm for Minimum Capacitated Dominating Set Problem," Mathematics, MDPI, vol. 8(11), pages 1-26, October.
    12. Salcedo-Sanz, S. & Cuadra, L., 2019. "Quasi scale-free geographically embedded networks over DLA-generated aggregates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1286-1305.
    13. M. Tadaros & A. Migdalas, 2022. "Bi- and multi-objective location routing problems: classification and literature review," Operational Research, Springer, vol. 22(5), pages 4641-4683, November.
    14. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    15. Mustafa Çağlayan & János Pintér, 2013. "Development and calibration of a currency trading strategy using global optimization," Journal of Global Optimization, Springer, vol. 56(2), pages 353-371, June.
    16. Raka Jovanovic & Antonio P. Sanfilippo & Stefan Voß, 2022. "Fixed set search applied to the multi-objective minimum weighted vertex cover problem," Journal of Heuristics, Springer, vol. 28(4), pages 481-508, August.
    17. Lin, B.M.T. & Lu, C.Y. & Shyu, S.J. & Tsai, C.Y., 2008. "Development of new features of ant colony optimization for flowshop scheduling," International Journal of Production Economics, Elsevier, vol. 112(2), pages 742-755, April.
    18. Toloo, Mehdi & Mensah, Emmanuel Kwasi & Salahi, Maziar, 2022. "Robust optimization and its duality in data envelopment analysis," Omega, Elsevier, vol. 108(C).
    19. Yenny Villuendas-Rey & José L. Velázquez-Rodríguez & Mariana Dayanara Alanis-Tamez & Marco-Antonio Moreno-Ibarra & Cornelio Yáñez-Márquez, 2021. "Mexican Axolotl Optimization: A Novel Bioinspired Heuristic," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    20. Guillermo Cabrera-Guerrero & Andrew J. Mason & Andrea Raith & Matthias Ehrgott, 2018. "Pareto local search algorithms for the multi-objective beam angle optimisation problem," Journal of Heuristics, Springer, vol. 24(2), pages 205-238, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:319:y:2022:i:2:d:10.1007_s10479-021-04232-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.