Abstract
Topology optimization problem, which involves many design variables, is commonly solved by finite element method, a method must recalculate structure-stiffness matrix each time of analysis. OC method is a good way to solve topology optimization problem, nevertheless, it can not solve multiobjective topology optimization problems. This paper introduces an effective solution to Multi-objective topology optimization problems by using Neural Network algorithms to improve the traditional OC method. Specifically, in each iteration, calculate the new neural network link weight vector by using the previous link weight vector in the last iteration and the compliance vector in the last time of optimization, then work out the impact factor of each optimization objective on the overall objective of the optimization in order to determine the optimal direction of each design variable.
This paper is supported by the National Basic Research Program of China (973 Program), No. 2004CB719405 and the National Natural Science Foundation of China, No. 50305008.
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References
Zhou, M., Rozvany, G.I.N.: The COC algorithm, Part II: topological, geometry and generalized shape optimization. Computer Methods in Applied Mechanics and Engineering 89, 197–224 (1991)
Zhou, M., Rozvany, G.I.N.: DCOC: an optimality criteria method for large systems, Part I: Theory. Structural optimization 5, 12–25 (1995)
Zhou, M., Rozvany, G.I.N.: DCOC: an optimality criteria method for large systems, Part II: Algorithm. Structural optimization 6, 250–262 (1995)
Sigmund, O.: A 99 line topology optimization code written in Matlab. Struct. Multidisc. Optim. 21, 120–127 (2001)
Aguilar Madeira, J., Rodrigues, H.C., Pina, H.: Multiobjective topology optimization of structures using genetic algorithms with chromosome repairing. Struct. Multidisc. Optim. 32, 31–39 (2006)
Luo, Z., Chen, L.P., Yang, J.Z., Zhang, Y.Q.: Multiple stiffness topology optimizations of continuum structures. Int. J. Adv. Manuf. Technol. 30, 203–214 (2006)
Chen, T.Y., Shieh, C.C.: Fuzzy multiobjective topology optimization. Computers and Structures 78, 459–466 (2000)
Kim, T.S., Kim, Y.Y.: Multiobjective topology optimization of a beam under torsion and distortion. AIAA Journal 40, 376–381 (2002)
Mohandans, S.U., Phelps, T.A., Ragsdell, K.M.: Structural optimization using a fuzzy goal programming approach. Computers and Structures 37, 1–8 (1990)
Min, S., Nishiwaki, S., Kikuchi, N.: Unified topology design of static and vibrating structures using multiobjective optimization. Computers and Structures 75, 93–116 (2000)
Fujii, H., Ito, H., Aihara, K., Ichinose, N., Tsukada, M.: Dynamical cell assembly hypothesis - Theoretical possibility of spatio-temporal coding in the cortex. Neural Networks 9, 1303–1350 (1996)
Brown, T.H., Kairiss, E.W., Keenan, C.L.: Hebbian synapses: Biophysical mechanisms and algorithms. Annual Review of Neuroscience 13, 475–511 (1990)
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Shao, X., Chen, Z., Fu, M., Gao, L. (2007). Multi-objective Topology Optimization of Structures Using NN-OC Algorithms. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_26
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DOI: https://doi.org/10.1007/978-3-540-72395-0_26
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