Abstract
We have seen that Bionic Optimization can be a powerful tool when applied to problems with non-trivial landscapes of goals and restrictions. This, in turn, led us to a discussion of useful methodologies for applying this optimization to real problems. On the other hand, it must be stated that each optimization is a time consuming process as soon as the problem expands beyond a small number of free parameters related to simple parabolic responses. Bionic Optimization is not a quick approach to solving complex questions within short times. In some cases it has the potential to fail entirely, either by sticking to local maxima or by random exploration of the parameter space without finding any promising solutions. The following sections present some remarks on the efficiency and limitations users must be aware of. They aim to increase the knowledge base of using and encountering Bionic Optimization. But they should not discourage potential users from this promising field of powerful strategies to find good or even the best possible designs.
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References
Berke, L., Patnaik, S. N., & Murthy, P. L. N. (1993). Optimum design of aerospace structural components using neural networks. Computers and Structures, 48, 1001–1010.
Coelho, L. D. S., & Mariani, V. C. (2006). Particle swarm optimization with Quasi-Newton local search for solving economic dispatch problem. IEEE International Conference on Systems, Man and Cybernetics, 4, 3109–3113.
Güttler, H. (2014). TFLOP performance for ANSYS mechanical 2.0. Nürnberg: ANSYS Conference 32. CADFEM Users Meeting.
Lagaros, N. D., & Papadrakakis, M. (2004). Learning improvement of neural networks used in structural optimization. Advances in Engineering Software, 35, 9–25.
Ohlhorst, F. J. (2012, December). Optimize workstations for faster simulations. In Desktop Engineering – Technology for Design Engineering (pp. 68–70). Retrieved June 11, 2015, from http://www.engineerquiz.com/issues/level5_desktopengineering_201212.pdf
Plevris, V., & Papadrakakis, M. (2011). A hybrid particle swarm—gradient algorithm for global structural optimization. Computer-Aided Civil and Infrastructure Engineering, 26, 48–68.
Rechenberg, I. (1994). Evolutionsstrategie’94. Stuttgart: Frommann-Holzboog.
Steinbuch, R. (2010). Successful application of evolutionary algorithms in engineering design. Journal of Bionic Engineering, 7(Suppl), 199–211.
Surjanovic, S., & Bingham, D. (2015). Virtual library of simulation experiments: Test functions and datasets. Retrieved May 12, 2015, from http://www.sfu.ca/~ssurjano
Widmann, Ch. (2012). Strukturoptimierung mit Neuronalen Netzen. Master thesis, Reutlingen University.
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Popova, T., Kmitina, I., Steinbuch, R., Gekeler, S. (2016). Problems and Limitations of Bionic Optimization. In: Steinbuch, R., Gekeler, S. (eds) Bionic Optimization in Structural Design. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46596-7_3
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DOI: https://doi.org/10.1007/978-3-662-46596-7_3
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