Maio 2014 vol. 1 num. 1 - 10th World Congress on Computational Mechanics

Full Article - Open Access.

Idioma principal

PERFORMANCE ASSESSMENT OF MODERN HEURISTIC ALGORITHMS USED IN STRUCTURAL OPTIMIZATION

Begambre, O. ;

Full Article:

In the recent literature, a description of the conditions under which an algorithm can be expected to be successful or fail is not often included in the studies. Because of this, in this work we compare the performance, in terms of precision and stability, of five heuristic algorithms in order to obtain valid statistical results. The problem instance we have used to do the comparison is the optimal weight design of a set of two dimensional steel frames. The new Bacterial Foraging Optimization Algorithm (BFOA), the Bees algorithm (BA), the Particle Swarm Optimization (PSO),the Genetic algorithm (GA) and the Simulated Annealing Algorithm (SAA) were tested. This work also provides an initial assessment in terms of the success rate and quality of the solution.

Full Article:

Download (PDF)

Palavras-chave: Structural Optimization, Bacterial Foraging Optimization Algorithm (BFOA), Bees algorithm (BA), the Particle Swarm Optimization (PSO), Simulated Annealing Algorithm (SAA).,

Palavras-chave:

DOI: 10.5151/meceng-wccm2012-19188

Referências bibliográficas
  • [1] K.M. Passino. “Biomimicry of bacterial foraging for distributed optimization and control”. IEEE Control Systems Magazine. 52–67, 2002.
  • [2] S. Das, A. Biswa, S. Dasgupta, and A. Abraham. “Bacterial Foraging Optimization Algorithm: Theoretical Foundations”. Analysis, and Applications. tutorial. softcomputing.net/bfoa-chapter.pdf. Accessed 10/01/201
  • [3] D. Karaboga, “An idea based on honey bee swarm for numerical optimization”. Erciyes University, Technical Report-TR06, Kayseri, Turkey, 2005.
  • [4] W. Gao, S. Liu, L. Huang. “A global best artificial bee colony algorithm for global optimization”. Journal of Computational and Applied Mathematics. 236, 2741–2753, 2012.
  • [5] J. Kennedy, R. Eberhart. “Particle swarm optimization”. Proc. IEEE Int. Conf. Neural Networks. 4, 1942-1948, 199
  • [6] Y. Shi, R.C. Eberhart. “Parameter selection in particle swarm optimization”. Evolutionary programming VII, Lecture notes in computer science. 1447, 591-600, In: Porto, V. Saravanan, N., Waagen, D., Eiben, A. (Eds). Berlin: Springer, 1998.
  • [7] R. Gabor, E. Aniko. “Genetic algorithms in computer aided design”. Computer-Aided Desing. 35 , 709-726, 2003.
  • [8] Kirkpatrick, S., Gelatt, A., Vecchi, M.P. “Optimization by simulated annealing”. Science. 220, 671-680, 1983.
  • [9] Cerny, V. “Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm”. J. Opt. Theory Appl. 45, 41-51, 1985.
  • [10] G. Sanchez, P. Martinez. “Minimum Cost Design with Advanced Analysis for Elastic Planar Steel Frames”. In: Proceedings of The Eighth International Conference on Computational Structures Technology. B.H.V. Topping, G. Montero and R. Montenegro (Editors). Civil-Comp Press, Stirlingshire, UK, Paper 120, 2006.
  • [11] O. Begambre, J.E. Laier. “A hybrid particle swarm optimization-simplex algorithm (PSOS) for structural damage identification”. Advances in Engineering Software. 40, 883- 891, 2009.
Como citar:

Begambre, O.; "PERFORMANCE ASSESSMENT OF MODERN HEURISTIC ALGORITHMS USED IN STRUCTURAL OPTIMIZATION", p. 3146-3154 . In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 2358-0828, DOI 10.5151/meceng-wccm2012-19188

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações