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Tytuł artykułu

Artificial neural networks and evolutionary algorithms in engineering design

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Purpose of this paper is investigation of optimization strategies eligible for solving complex engineering design problems. An aim is to develop numerical algorithms for solving optimal design problems which may contain real and integer variables, a number of local extremes, linear- and non-linear constraints and multiple optimality criteria. Design/methodology/approach: The methodology proposed for solving optimal design problems is based on integrated use of meta-modeling techniques and global optimization algorithms. Design of the complex and safety critical products is validated experimentally. Findings: Hierarchically decomposed multistage optimization strategy for solving complex engineering design problems is developed. A number of different non-gradient methods and meta-modeling techniques has been evaluated and compared for certain class of engineering design problems. The developed optimization algorithms allows to predict the performance of the product (structure) for different design and configurations parameters as well as loading conditions. Research limitations/implications: The results obtained can be applied for solving certain class of engineering design problems. The nano- and microstructure design of materials is not considered in current approach. Practical implications: The methodology proposed is employed successfully for solving a number of practical problems arising from Estonian industry: design of car frontal protection system, double-curved surface forming process modeling, fixings for frameless glazed structures, optimal design of composite bathtub (large composite plastics), etc. Originality/value: Developed numerical algorithms can be utilised for solving a wide class of complex optimization problems.
Rocznik
Strony
88--95
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
  • Department of Machinery, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia
autor
  • Department of Machinery, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia
autor
  • Department of Machinery, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia
autor
  • Department of Machinery, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia
autor
  • Department of Machinery, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia
Bibliografia
  • [1] S. Hernandez, Structural optimization 1960-2010, Computer Technology Review 2 (2010) 177-222.
  • [2] M.P. Bendsoe, O. Sigmund, Topological optimization: theory, methods and applications, Springer-Verlag, Berlin, 2004, 370.
  • [3] A.J. Keane, P.B. Nair, Computational Approaches for Aerospace Design, John Wiley&Sons, Ltd, West Sussex, 2005, 582.
  • [4] J. Sobieszczanski-Sobieski, T.D. Altus, R.R. Sandusky, Bi-level Integrated system synthesis for concurrent and distributed processing, AIAA Journal 20 (2003) 1291-1299.
  • [5] R.T. Hafka, Combining global and local approximations, AIAA Journal 29 (2003) 1523-1525.
  • [6] D.H. Bassir, J.L. Zapico, M.P. Gonzales, R. Alonso, Identification of a spatial linear model based on earthquake-induced data and genetic algorithm with parallel selection, International Journal of Simulation and Multidisciplinary Design Optimization 1 (2007) 39-48.
  • [7] M. Pohlak, J. Majak, M. Eerme, Optimization of car frontal protection system. International, Journal of Simulation and Multidisciplinary Design Optimization 1 (2007) 31-38.
  • [8] M. Pohlak, J. Majak, K. Karjust, R. Kuttner, Multicriteria optimization of large composite parts. Composite Structures 92 (2010) 2146-2152.
  • [9] R. Kicinger, T. Arciszewski, K.-A. De Jong, Evolutionary computation and structural design: A survey of the state of the art, Computers & Structures 83/23-24 (2005) 1943-1978.
  • [10] J.R. Koza, Genetic programming: on the programming of computers by means of natural selection, Cambridge, Mass.: MIT Press, 1992.
  • [11] J.C. Spall, Introduction to stochastic search and optimization, Wiley-Interscience, 2003.
  • [12] K. Deb, S. Tiwari, Multi-objective optimization of a leg mechanism using genetic algorithms, Engineering Optimization 37/4 (2005) 325-350.
  • [13] M. Bhattacharya, Surrogate based evolutionary algorithm for design optimization, Proceeding of World Academy of Science, Engineering and Technology 10 (2005) 52-57.
  • [14] Y. Jin, M. Olhofer, B. Sendhoff, A framework for evolutionary optimization with approximate fitness functions, IEEE Transactions on Evolutionary Computation 6/5 (2002) 481-494.
  • [15] M. Srinivas, L.M. Patnaik, Adaptive probabilities of crossover and mutations in GAs, IEEE Transactions on Systems, Man, and Cybernetics 24 (1994) 656-667.
  • [16] Q. Yuan, Z. He, H. Leng, A hybrid genetic algorithm for a class of global optimization problems with box constraints, Applied Mathematics and Computation 197 (2008) 924-929.
  • [17] Y.T. Kao, E. Zahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions, Applied Soft Computing 8 (2008) 849-857.
  • [18] S. Kumar, R. Naresh, Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem, Electrical Power and Energy Systems 29/10 (2007) 738-747.
  • [19] J.W. Kim, S.W. Kim, New encoding/converting methods of binary GA/real coded GA, IEICE Transactions of Fundamentals 88/6 (2005) 1554-1564.
  • [20] K. Deb, Multi-objective optimization using evolutionary algorithms, Chichester, John Wiley & Sons, New York, 2002.
  • [21] C.A. Coello, An updated survey of GA-based multiobjective optimization techniques, ACM Computing Surveys 32/2 (2000) 109-143.
  • [22] M. Pohlak, J. Majak, M. Eerme, Optimization study of car frontal protection system, In: ASMDO, Proceedings of the 1st International Conference on Multidisciplinary Optimization and Applications, Besancon, 2007
  • [23] N. Stander, W. Roux, T. Eggleston, K. Craig, LS-OPT user’s manual, Livermore Software Technology Corporation, 2006.
  • [24] AAA. Alghamdi, Collapsible impact energy absorbers: an overview, In Thin-Walled Structures 39 (2001) 189-213.
  • [25] J. De Kanter, Energy absorption of monolithic and fibre reinforced aluminium cylinders, Delft University of Technology, PhD Thesis, 2006.
  • [26] G. Lu, T.X. Yu, Energy absorption of structures and materials, Woodhead Publishing Limited, Cambridge, 2003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-720a6786-52c4-423b-aa4e-9a25eed3c8a6
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