Advances in development of dedicated evolutionary algorithms for large non-linear constrained optimization problems

• Autorzy: Głowacki M. , Orkisz J.
• Języki publikacji: EN

Abstrakty

EN

Efficient optimization algorithms are of great importance in many scientific and engineering applications. This paper considers development of dedicated Evolutionary Algorithms (EA) based approach for solving large, non-linear, constrained optimization problems. The EA are precisely understood here as decimal-coded Genetic Algorithms consisting of three basic operators: selection, crossover and mutation, followed by several newly developed calculation speed-up techniques. Efficiency increase of the EA computations may be obtained in several ways, including simple concepts proposed here like: solution smoothing and balancing, a posteriori solution error analysis, non-standard use of distributed and parallel calculations, and step-by-step mesh refinement. Efficiency of the proposed techniques has been evaluated using several benchmark tests. These preliminary tests indicate significant speed-up of the large optimization processes involved. Considered are applications of the EA to the sample problem of residual stresses analysis in elastic-plastic bodies being under cyclic loadings, and to a wide class of problems resulting from the Physically Based Approximation (PBA) of experimental data.

Słowa kluczowe

EN

evolutionary algorithms; large non-linear constrained optimization; solution efficiency increase

PL

algorytmy ewolucyjne

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: IPPT Reports on Fundamental Technological Research
• Rocznik: 2013
• Tom: nr 4
• Strony: 25--29
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Głowacki M.

• Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Reymonta 4, 30-059 Kraków, Poland

autor

Orkisz J.

• Institute for Computational Civil Engineering Cracow University of Technology Warszawska 24, 31-155 Kraków, Poland

Bibliografia

• 1. J. ORKISZ, M. GŁOWACKI, On acceleration of evolutionary algorithms taking advantage from aposteriori error analysis, Computing and Informatics, accepted for publication.
• 2. J. ORKISZ, M. GŁOWACKI, On certain improvements for evolutionary algorithms applied to residual stresses analysis, in CMM 2013 Short Papers, T. Łodygowski et al. [Eds.], Poznań University of Technology, Poznań, MS05-7-8, 2013.
• 3. J. ORKISZ, M. GŁOWACKI, On stress reconstruction using experimental data the PBA and accelerated EA, in ECCOMAS 2013. Conference Proceedings, 'L. Waszczyszyn, L. Ziemiański [Eds.], Rzeszów University of Technology, Rzeszów, 43-44, 2013.
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