Evolutionary computation in optimization and identification

  • Tadeusz Burczyński Institute of Computer Modelling, Cracow University of Technology
  • Witold Beluch Silesian University of Technology
  • Adam Długosz Silesian University of Technology
  • Wacław Kuś Silesian University of Technology
  • Marek Nowakowski Silesian University of Technology
  • Piotr Orantek Silesian University of Technology

Abstract

The aim of the paper is to present the application of the evolutionary algorithms to selected optimization and identification problems of mechanical systems. The coupling of evolutionary algorithms with the finite element method and the boundary element method creates a new artificial intelligence technique that is very suitable in computer aided optimal design and defect detection. Several numerical examples for optimization and identification are presented.

Keywords

evolutionary algorithms, finite element method, boundary element method, optimization, identification,

References

[1] J. Arabas. Lectures on Evolutionary Algorithms. WNT, Warszawa, 2001.
[2] G. Beer, J.O. Watson. Introduction to Finite and Boundary Element Methods for Engineering. Wiley. Chichester, 1992.
[3] W. Beluch. Sensitivity Analysis and Evolutionary Optimization of Cracked Mechanical Structures (in Polish) . Ph.D.Thesis, Mechanical Engineering Faculty, Silesian University of Technology, Gliwice, 2000.
[4] W. Beluch. Crack identification using evolutionary algorithms. In: T . Burczyński, W. Cholewa, eds., Methods of Artificial Intelligence in Mechanics and Mechanical Engineering, 97- 100. Gliwice, 2000.
[5] T. Burczyński. Applications of BEM in sensitivity analysis and optimization. Computational Mechanics, 13: 29- 44, 1993.
Published
Feb 22, 2023
How to Cite
BURCZYŃSKI, Tadeusz et al. Evolutionary computation in optimization and identification. Computer Assisted Methods in Engineering and Science, [S.l.], v. 9, n. 1, p. 3-20, feb. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/1137>. Date accessed: 18 july 2024.
Section
Articles

Most read articles by the same author(s)