Janusz Orkisz
ABSTRACT
In this paper we briefly discuss new advances in development of an efficient approach based on Evolutionary Algorithms (EA) for solving a wide class of large, non-linear, constrained optimization problems. Two important applications to engineering mechanics are intended, namely residual stress analysis in railroad rails, and vehicle wheels, as well as a wide class of problems resulting from the Physically Based Approximation of experimental data. However, the primary objective of our long-term research is to obtain significant acceleration of the EA applied to large optimization problems, and to provide ability to solve problems when the standard EA fail. The efficiency of new speed-up techniques proposed was examined using several simple but demanding benchmark problems of computational mechanics. Results obtained so far indicate possibility of practical application of the new approach to real large engineering problems.
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Cross Ref
- Engelbrecht, A. P. 2007. Computational intelligence: an introduction. Wiley, Chichester. Google ScholarDigital Library
- Glowacki, M. and Orkisz, J. 2015. On increasing computational efficiency of evolutionary algorithms applied to large optimization problems. 2015 IEEE C. Evol. Comput. 2639--2646.Google Scholar
- Karmowski, W. and Orkisz, J. 1993. Physically based method of enhancement of experimental data -- concepts, formulation and application to identification of residual stresses. In Inverse Problems in Engineering Mechanics. IUATAM Symposium (Tokyo, 1992), Springer-Verlag, 61--70.Google Scholar
- Murata, T. et al. 2015. Analytical estimation of the convergence point of populations, 2015 IEEE C. Evol. Comput, 2619--2624.Google Scholar
- Orkisz, J. 1992. Prediction of actual residual stresses by constrained minimization of energy. In Residual Stress in Rails, vol. 2, Kluwer Academic Publisher, 101--124.Google Scholar
Index Terms
- On Development of a New Approach for EA Acceleration in Chosen Large Optimization Problems of Mechanics
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