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Genetic algorithms in mesh optimization for visualization and finite element models

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Abstract

This paper investigates the use of a genetic algorithm (GA) to perform the large-scale triangular mesh optimization process. This optimization process consists of a combination of mesh reduction and mesh smoothing that will not only improve the speed for the computation of a 3D graphical or finite element model, but also improve the quality of its mesh. The GA is developed and implemented to replace the original mesh with a re-triangulation process. The GA features optimized initial population, constrained crossover operator, constrained mutation operator and multi-objective fitness evaluation function. While retaining features is important to both visualization models and finite element models, this algorithm also optimizes the shape of the triangular elements, improves the smoothness of the mesh and performs mesh reduction based on the needs of the user.

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References

  1. Caponetto R, Fortuna L, Graziani S, Xibilia MG (1993) Genetic algorithms and applications in system engineering: a survey. Trans Inst Meas Control 15(3):143–156

    Article  Google Scholar 

  2. Rudolph G (1994) Convergency analysis of canonical genetic algorithms. IEEE Trans Neural Netw 5(1):96–101

    Article  Google Scholar 

  3. Michalewicz Z (1998) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, Berlin Heidelberg New York, ISBN 3-540-60676-9

  4. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  5. Holland JH (1975) Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor

    Google Scholar 

  6. Dorsey RE, Mayer WJ (1995) Genetic algorithms for estimation problems with multiple optima, non-differentiability, and other irregular features. J Bus Econ Stat 13(1):53–66

    Article  Google Scholar 

  7. Liu X, Begg DW, Fishwick RJ (1998) Genetic approach to optimal topology/controller design of adaptive structures. Int J Numer Methods Eng 41(5):815–830

    Article  MATH  Google Scholar 

  8. Absaloms H, Tomikawa T (1996) Surface reconstruction by triangulation using GA. In: Proceedings of the 20th international conference on computers and industrial engineering, Kyongju, Korea, 6–9 October 1996, pp 441–444

  9. Qin KH, Wang WP, Gong ML (1997) A genetic algorithm for the minimum weight triangulation. In: Proceedings of the IEEE conference on evolutionary computation, Indianapolis, IN, USA, 13–16 April 1997, pp 541–546

  10. Hamann B (1994) A data reduction scheme for triangulated surfaces. Comput Aided Geom Des 11(2):197–214

    Article  MATH  MathSciNet  Google Scholar 

  11. Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W (1993) Mesh optimization. In: ACM SIGGRAPHICS Proceedings, vol 1, pp 19–26

  12. Ronfard R, Rossignace J (1996) Full-range approximations of triangulated polyhedra. Proceedings of EUROGRAPHIVS’96. Comput Graph Forum 15(3):67–76

    Article  Google Scholar 

  13. Gieng TS, Joy KI, Schussman GL, Trotts IJ (1998) Constructing hierarchies for triangle meshes. IEEE Trans Vis Comput Graph 4(2):145–161

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Institute of High Performance Computing, 1 Science Park Road #01-01, The Capricorn, Singapore Science Park II, Singapore, Singapore, 117528

    C. S. Chong & H. P. Lee

  2. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore, 119260

    H. P. Lee & A. Senthil Kumar

Authors
  1. C. S. Chong
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  2. H. P. Lee
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  3. A. Senthil Kumar
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Correspondence to C. S. Chong.

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Chong, C.S., Lee, H.P. & Senthil Kumar, A. Genetic algorithms in mesh optimization for visualization and finite element models. Neural Comput & Applic 15, 366–372 (2006). https://doi.org/10.1007/s00521-006-0041-2

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  • DOI: https://doi.org/10.1007/s00521-006-0041-2

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