Skip to main content
SpringerLink
Log in

Mesh smoothing of complex geometry using variations of cohort intelligence algorithm

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Several approaches including optimization based methods were developed for mesh quality improvement using only node movement, keeping intact the element connectivity. In this research, a socio-inspired optimization approach referred to as cohort intelligence (CI) was investigated for mesh smoothing. Minimization of summation of condition numbers of all elements was the final aim. The geometrical boundaries of the object defined the surface and edge constraints for movement of external nodes. Movement of internal nodes was completely governed by variations of CI algorithm, viz. roulette wheel, follow best, follow better, alienation and random selection, follow worst and follow itself. The approach was demonstrated with pentagonal prism, hexagonal prism and hexagonal prism with hole. The performance of follow best and roulette wheel variations of CI algorithm was observed to be satisfactory as compared to other variations of the algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Acikgoz N, Bottasso CL, Detomi D (2004) Metric based mesh optimization using simulated annealing. In: 4th European congress on computational methods in applied sciences and engineering (ECCOMAS 2004), pp 1–11

  2. Belyaev A, Ohtake Y (2003) A comparison of mesh smoothing methods. In: Israel–Korea bi-national conference on geometric modeling and computer graphics, vol 2

  3. Bern M, Eppstein D (1995) Mesh generation and optimal triangulation. In: Computing in Euclidean geometry, pp 47–123

  4. Dhavle SV, Kulkarni AJ, Shastri A, Kale IR (2016) Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2683-z

    Article  Google Scholar 

  5. Ho-Le K (1988) Finite element mesh generation methods: a review and classification. Comput Aided Des 20(1):27–38

    Article  Google Scholar 

  6. Holder M, Richardson J (1998) Genetic algorithms, another tool for quad mesh optimization. In: IMR, pp 497–504

  7. Kale IR, Kulkarni AJ (2017) Cohort intelligence algorithm for discrete and mixed variable engineering problems. Int J Parallel Emerg Distrib Syst. https://doi.org/10.1080/17445760.2017.1331439

    Article  Google Scholar 

  8. Knupp PM (2001) Algebraic mesh quality metrics. SIAM J Sci Comput 23(1):193–218

    Article  MathSciNet  Google Scholar 

  9. Knupp PM (2003) A method for hexahedral mesh shape optimization. Int J Numer Methods Eng 58(2):319–332

    Article  Google Scholar 

  10. Kovalev K (2005) Unstructured hexahedral non-conformal mesh generation. Faculty of Engineering, Vrije Universiteit Brussel, Brussel

    Google Scholar 

  11. Kolukula SS (2016) PlotMesh source code (version 2), Mathsworks [Source code]. https://sites.google.com/site/kolukulasivasrinivas/. Accessed 17 Aug 2018

  12. Krishnasamy G, Kulkarni AJ, Paramesran R (2014) A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Syst Appl 41(13):6009–6016

    Article  Google Scholar 

  13. Kulkarni AJ, Baki MF, Chaouch BA (2016) Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems. Eur J Oper Res 250(2):427–447

    Article  MathSciNet  Google Scholar 

  14. Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self-supervised learning behavior. In: 2013 IEEE international conference on systems, man, and cybernetics (SMC), pp 1396–1400

  15. Kulkarni O, Kulkarni N, Kulkarni AJ, Kakandikar G (2016) Constrained cohort intelligence using static and dynamic penalty function approach for mechanical components design. Int J Parallel Emerg Distrib Syst. https://doi.org/10.1080/17445760.2016.1242728

    Article  Google Scholar 

  16. Kulkarni AJ, Krishnasamy G, Abraham A (2017) Cohort intelligence: a socio-inspired optimization method. Springer International Publishing, Switzerland

    Book  Google Scholar 

  17. Kulkarni AJ, Shabir H (2016) Solving 0–1 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybern 7(3):427–441

    Article  Google Scholar 

  18. Patankar NS, Kulkarni AJ (2018) Variations of cohort intelligence. Soft Comput 22(6):1731–1747

    Article  Google Scholar 

  19. Parthasarathy VN, Kodiyalam S (1991) A constrained optimization approach to finite element mesh smoothing. Finite Elem Anal Des 9(4):309–320

    Article  Google Scholar 

  20. Pyzara A, Bylina B, Bylina J (2011) The influence of a matrix condition number on iterative methods’ convergence. In: 2011 federated conference on computer science and information systems (FedCSIS). IEEE, pp 459–464

  21. Sarmah DK, Kulkarni AJ (2017) Image steganography capacity improvement using cohort intelligence and modified multi-random start local search methods. Arab J Sci Eng 43(8):3927–3950

    Article  Google Scholar 

  22. Sarmah DK, Kulkarni AJ (2018) JPEG based steganography methods using cohort intelligence with cognitive computing and modified multi random start local search optimization algorithms. Inf Sci 430–431:378–396

    Article  Google Scholar 

  23. Sastry SP, Shontz SM (2012) Performance characterization of nonlinear optimization methods for mesh quality improvement. Eng Comput 28(3):269–286

    Article  Google Scholar 

  24. Sastry SP, Shontz SM (2009) A comparison of gradient-and hessian-based optimization methods for tetrahedral mesh quality improvement. In: Proceedings of 18th international meshing roundtable, pp 631–648

  25. Shah P, Agashe S, Kulkarni AJ (2018) Design of fractional PID controller using cohort intelligence method. Frontiers Inf Technol Electronic Eng 19(3):437–445

    Article  Google Scholar 

  26. Yalcin E, Yilmaz AE, Kuzuoglu M (2002) Performance comparison of various hexahedral element quality metrics via parametric distortion of an ideal element. Int J Comput Methods 10(04):1350017

    Article  MathSciNet  Google Scholar 

  27. Yilmaz AE, Kuzuoglu M (2009) A particle swarm optimization approach for hexahedral mesh smoothing. Int J Numer Methods Fluids 60(1):55–78

    Article  Google Scholar 

  28. Zhang S (2005) Subtetrahedral test for the positive Jacobian of hexahedral elements. Preprint. http://www.math.udel.edu/~szhang/research/p/subtettest.pdf. Accessed 17 Aug 2018

Download references

Author information

Authors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, MH, 412 115, India

    Mandar S. Sapre, Anand J. Kulkarni, Lakshmanan Chettiar, Ishani Deshpande & Bharat Piprikar

  2. Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada

    Anand J. Kulkarni

Authors
  1. Mandar S. Sapre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anand J. Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lakshmanan Chettiar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ishani Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bharat Piprikar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand J. Kulkarni.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sapre, M.S., Kulkarni, A.J., Chettiar, L. et al. Mesh smoothing of complex geometry using variations of cohort intelligence algorithm. Evol. Intel. 14, 227–242 (2021). https://doi.org/10.1007/s12065-018-0166-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-018-0166-0

Keywords

Search

Navigation