Structural Optimisation using a Hybrid Cellular Automata (HCA) Algorithm

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The Hybrid Cellular Automata (HCA) algorithm has been used by several researchers to optimise structures during the last decade. Close observation of their work shows that the proposed optimisation algorithms are sensitive to the controller (local rule), the design variable and the field variable used. The aim of this work is to identify and understand the important parameters when using the HCA algorithm to optimise structures. For static loading, it is shown that the most important parameters are the design variable, the constraints on the design variable, the local rule, and the mesh density of the structure. The choice of the design variable affects the selection of the target value and the homogeneity of the resulting optimum structure. With constraints on the design variable, it is shown that the algorithm cannot always drive the structure to an optimum solution, as stresses in the resulting structure can be significantly higher than expected. Besides, the choice of the local rule and the mesh density of the structure can affect the convergence rate and may cause the algorithm to arrive at a local optimum rather than the global optimum solution.

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Edited by:

Patrick Sean Keogh

Pages:

93-100

Citation:

C.M. Chia et al., "Structural Optimisation using a Hybrid Cellular Automata (HCA) Algorithm", Applied Mechanics and Materials, Vols. 5-6, pp. 93-100, 2006

Online since:

October 2006

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$38.00

[1] K. Worden and G.R. Tomlinson: in Proceedings of International Symposium on Microsystems, Intelligent Materials and Robots, Sendai, Japan (1995).

[2] A. Tovar, G.L. Niebur, M., Sen and J.E. Renaud: in 45 th AIAA/ASME/ASCE/AHS/ASC Structure, Structural Dynamics & Materials Conference, California (2004).

DOI: https://doi.org/10.2514/6.2004-1914

[3] D.E. Goldberg: Genetic algorithms in search, optimization, and machine learning. AddisonWesley Publishing Company, Canada (1989).

[4] A. Marczyk: Genetic Algorithms and Evolutionary Computation [online]. Available from: http: /www. talkorigins. org/faqs/genalg/genalg. html [Accessed 19th May 2005].

[5] M. Mitchell: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1996).

[6] A. Adamatzky: Identification of Cellular Automata. Taylor and Francis, London (1994).

[7] S. Bandini, B. Chopard and M. Tomassini (Eds. ): Cellular Automata: Proceedings, 5 th International Conference on Cellular Automata for Research and Industry, ACRI (2002).

[8] P.P. Chaudhuri, D.R. Chowdhury, S. Nandi and S. Chattopadhay: Additive Cellular Automata: Theory and Application. IEEE Computer Society Press, California (1997).

[9] S. Wolfram: Cellular Automata and Complexity: collected papers. Addison-Wesley (1994).

[10] S. Wolfram: A New Kind of Science. Wolfram Media, Canada (2002).

[11] X. Hu: Particle swarm optimization [online]. Available from: http: /www. swarmintelligence. org/ [Accessed 19th May 2005].

[12] J. Kennedy and R.C. Eberhaurt: in Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia.

[13] P. Pomeroy, P: An introduction to particle swarm optimization [online]. Available from: http: /www. adaptiveview. com/articles/ipsop1. html [Accessed 19th May 2005].

[14] M. Dorigo: Ant colony optimization [online]. Available from: http: /iridia. ulb. ac. be/~mdorigo/ACO/index. html [Assessed 19th May 2005].

[15] P.E. Merloti: San Diego State University, Artificial Intelligence Technical Report, CS550.

[16] E. Kita and T. Toyoda: Structural and Multidisciplinary Optimization, Vol. 19 (2000), 64-73.

[17] N. Inou, N. Shimotai and T. Uesugi: in 2 nd European Conference on Smart Structures and Materials, Glasgow (1994).

[18] A. Tovar, N. Patel, A.K. Kaushik, G.A. Letona and J.E. Renaud: in 10 th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, New York (2004).

DOI: https://doi.org/10.2514/6.2004-4558

[19] A. Tovar, W.I. Quevedo, N.M. Patel and J.E. Renaud: in 6 th World Congress of Structural and Multidisciplinary Optimization (2005).

[20] H.A. Eschenauer and N. Olhoff: Applied Mechanics Reviews, Vol. 54 (2001), no 4, 331-390.

[21] G.I.N. Rozvany: Structural and Multidisciplinary Optimization, Vol. 21 (2001), 90-108.

[22] S.N. Patnaik and D.A. Hopkins: Computer Methods in Applied Mechanics and Engineering, Vol. 165 (1998), 215-221.

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