Computational intelligence methods in searching of stable configurations of nanostructures

• Autorzy: Mrozek A. , Kuś W. , Burczyński T.

Warianty tytułu

PL

Metody inteligencji obliczeniowej w wyznaczaniu stabilnych konfiguracji geometrycznych nanostruktur

• Języki publikacji: EN

Abstrakty

EN

Application of evolutionary algorithm, artificial immune system and particle swarm optimization in the minimization atomic cluster's total potential energy is presented in this work. These methods of computational intelligence simulate biological processes of the natural environment and organisms such as theory of evolution and biological immune systems and give a strong probability of finding the global optimum. Some examples and discussion on the results of optimization are also presented in this paper.

PL

W pracy opisane zostało zastosowanie wybranych metod inteligencji obliczeniowej (algorytmu ewolucyjnego, sztucznego systemu immunologicznego oraz optymalizacji rojem cząstek) do optymalizacji klastrów atomowych. Jako kryterium optymalizacji przyjęto minimalizację całkowitej energii potencjalnej nanostruktury. Do modelowania oddziaływań między atomowych użyto potencjałów Morse'a oraz Murrella-Mottrama. W pracy przedstawiono wybrane wyniki optymalizacji oraz ich interpretację.

Słowa kluczowe

EN

nanostructure; intelligence methods; evolutionary algorithms; artificial immune system; particle swarm optimization (PSO); modeling

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2011
• Tom: Vol. 11, No. 1
• Strony: 46--52
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Mrozek A.

autor

Kuś W.

autor

Burczyński T.

• Cracow University of Technology, Institute of Computer Science, Cracow, Poland,

Bibliografia

• Ahlrichs, R., Elliot, S.D., 1999, Clusters of Aluminium, a Density Functional Study, Chemical Physics, 1, 13-21.
• Burczyński, T., Mrozek, A., Górski, R., Kuś, W., 2010, Molecular Statics Coupled with the Subregion Boundary Element Metod in Multiscale Analysis, International Journal for Multiscale Computational Engineering, 8, 3, 319-330.
• de Castro, L.N., Timmis, J., 2003, Artificial Immune Systems as a Novel Soft Computing Paradigm, Soft Computing, 1, 8, 526-544.
• de Castro, L.N., Von Zuben, F.J., 2002, Learning and Optimization Using the Clonal Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6, 3, 239-251.
• Chan, F.T.S, Tiwari, M.K., 2007, Swarm Intelligence. Focus on Ant and Particle Swarm Optimization. I-Tech Education and Publishing.
• Chou, M.Y., Cohen, M.L., 1986, Electronic Shell Structure in Simple Metal Clusters. Physical Lett. A, 113, 420-424.
• Cox, H., Johnston, R.L., Murrell, J.N., 1997, Modelling of Surface Relaxation and Melting of Aluminium, Surf. Sci., 73, 67-84.
• Dugan, N., Erkoc, S., 2009, Genetic Algorithm Application to the Structural Properties of Si-Ge Mixed Clusters, Materials and Manufacturing Processes , 24, 3, 250-254.
• Girifalco, L.A., Weizer, V.G., 1959, Application of the Morse Potential Function to Cubic Metals, Physical Review, 114,3,687-690.
• Kennedy, J., Eberhart, R.C., Shi, Y., 2001, Swarm Intelligence, Morgan Kaufmann Publ.
• Lloyd, L.D., Johnston, R.L., 1998, Modelling aluminium clusters with an empirical many-body potential, Chemical Physics, 236, 107-121.
• Michalewicz, Z., 1996, Genetic algorithms + data structures = evolutionary algorithms. Springer-Verlag, Berlin.
• Morse, P.M., 1929, Diatomic Molecules According to the Wave Mechanics. II. Vibrational levels, Physical Review, 34, 57-64.
• Mrozek, A., Kuś, W., Burczyński, T., 2009, Optimization of Atomic Clusters Using Bio-Inspired Algorithms, 8th World Congress on Structural and Multidisciplinary Optimization, Lisbon.
• Mrozek, A., Kuś, W., Orantek, P., Burczyński, T., 2005, Prediction of the Aluminium Atoms Distribution Using Evolutionary Algorithm, Recent Advances in Methods of Artificial Intelligence, Gliwice.
• Murrell, J.N., Mottram, R.E., 1990, Potential Energy Functions for Atomic Solids, Molecular Physics, 69, 3, 571-585.
• Roberts, Ch., Johnston, R.L., Wilson, N.T., 2000, A Genetic Algorithm for the Structural Optimization of Morse Clusters, Theoretical Chemistry Accounts, 104, 123-130.
• Shao, X., Cheng, L., Cai, W., 2004, An Adaptive Immune Optimization Algorithm for Energy minimization Problems, Journal of Chemical Physics, 120, 24, 11401-11406.
• Wales, D.J., Doye, J.P.K., 1997, Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms, The Journal of Physical Chemistry A, 101, 5111-5116.
• Wierzchoń, S.T., 2001, Artificial Immune Systems, Theory and Applications, EXIT, Warszawa.
• Zhou, J-c, Li, W-j., Zhu, J-b., 2008, Particle Swarm Optimization Computer Simulation of Ni Clusters, Transactions of Nonferrous Metals Society of China, 18, 410-415.