Abstract
In our industry researches we often face very difficult problems where ordinary algorithms fail to find the global optimum. Mostly they have difficult, high-dimensional count, very large design space where even the concept of direction and distance are non-existent and have to be defined, the neighbourship in the feasible space also needs definition. In these cases, these terms are often defined and calculated by heuristic functions. On these problems the applied optimization methods often fail, they stuck in local optima, working very slowly and find suboptimal solution. So we decided to try to link optimization methods and create multi-level optimization methods to cope these problems. As a base concept in the first stage we use some simple, fast, rapidly converging algorithm, then some finer grade algorithm like population based swarm optimization method. In this paper, we will show and evaluate some multi-level optimization methods tested on several test functions, comparing the convergence and computational needs.
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References
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Acknowledgements
The research was supported by the Hungarian Scientific Research Fund OTKA T 109860 projects and was partially carried out in the framework of the Center of Excellence of Innovative Design and Technologies in Vehicle, Mechanical and Energy Engineering at the University of Miskolc within the EFOP-3.6.1-16-00011 “Younger and Renewing University – Innovative Knowledge City – institutional development of the University of Miskolc aiming at intelligent specialisation” project implemented in the framework of the Széchenyi 2020 program. The realization of this project is supported by the European Union, co-financed by the European Social Fund.
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Kota, L., Jármai, K. (2018). Application of Multilevel Optimization Algorithms. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_54
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DOI: https://doi.org/10.1007/978-3-319-67988-4_54
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