Some new methods in the evolutionary search of atomic cluster shape

• Autorzy: Stasikowski A. , Gwizdałła T.
• Konferencja: Evolutionary Computation and Global Optimization 2008 / National Conference (11 ; 2-4.06.2008 ; Szymbark, Poland)
• Języki publikacji: EN

Abstrakty

EN

The problem of atomic structure optimization related to the minimization of its total energy is a fundamental physical problem as well as hard computational task. For the few last years we have presented some observations concerning the advantages and drawbacks of EA used as a tool to solve such questions. In this paper we would like to present some new approaches devoted to improve the general, not problem oriented part of algorithm. The results obtained for two techniques: population migration and Opposition-Based Learning show that the specific operators, designed for the given problem are still the most important part of algorithm.

Słowa kluczowe

EN

global optimization; evolutionary algorithm; EA; nanoscopic system; migration of populations method; Opposition-Based Learning; evolutionary computation

PL

optymalizacja globalna; algorytm ewolucyjny; obliczenia ewolucyjne

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2008
• Tom: z. 165
• Strony: 213--220
• Opis fizyczny: Bibliogr. 8 poz., rys., wykr.

Twórcy

autor

Stasikowski A.

autor

Gwizdałła T.

• Dept. of Solid State Physics, University of Łódź, Pomorska 149/153, 90-236 Łódź, Poland,

Bibliografia

• [1] Stasikowski A., Gwizdałła T.M, Evolutionary strategies for atomic clusters modelling. Proceedings of National Conference on Evolutionary Algorithms and Global Optimization, Korbielów, 215, (2005).
• [2] Stasikowski A., Moneta M., Gwizdałła T., The structural properties of alkali metal atoms doped gas clusters., Radiat. Phys. Chem. 76, 607, (2007).
• [3] Stasikowski A., Badanie struktur klasterów atomowych z uwzględnieniem oddziaływań dwu i trój-ciałowych. Modelowanie metodą algorytmów ewolucyjnych. Łódź 2006.
• [4] Adamski K., Arabas J., Influence of intitation range on dynamic properties of an evolutionary algorithm., Proceedings of National Conference on Evolutionary Algorithms and Global Optimization, Korbielów, 1, (2005).
• [5] Tizhoosh H.R., Opposition-based learning: a new scheme for machine intelligence. in: International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA-2005), vol. I, Vienna, Austria, 695, (2005).
• [6] Roberts Ch., Johnston R.L., Wilson N.T., A genetic algorithm for the structural optimization of Morse clusters, Theor. Chem. Acc. 104, 123, (2000).
• [7] Deaven D.M., Tit N., Morris J.R., Ho K.M., Structural Optimization of Lennard-Jones clusters by a genetic algorithm., Chem. Phys. Lett. 256, 195, (1996).
• [8] Michalewicz Z., Genetics Algorithms + Data Structures = Evolution Programs. Springer-Verlag Berlin Heidelberg, (1996).