Searching of stable configurations of nanostructures using computational intelligence methods

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

Warianty tytułu

PL

Poszukiwanie stabilnych konfiguracji klastrów atomowych za pomocą metod inteligencji obliczeniowej

• Języki publikacji: EN

Abstrakty

EN

This paper deals with computational intelligence methods: evolutionary algorithms, artificial immune systems and the particle swarm optimization applied to the process of minimization of the potential energy of small nanostructures, such as atomic clusters. These algorithms simulate biological processes of the natural environment and organisms such as the theory of evolution and the biological immune systems. Mentioned approaches, generally, do not need any information about the gradient of the fitness function and give a strong probability of finding the global optimum. The main drawback of these methods is the long time of computations.

PL

W artykule przedstawiono zastosowanie wybranych metod inteligencji obliczeniowej (algorytmy ewolucyjne, sztuczne systemy immunologiczne, optymalizacja rojem cząstek) do minimalizacji energii potencjalnej klastrów atomowych. Do opisu oddziaływań międzyatomowych użyte zostały potencjały Morsa i Murrella-Mottrama.

Słowa kluczowe

EN

artificial immune systems; atomic clusters; evolutionary algorithms; Morse potential; Murrell-Mottram Potential; particle swarm optimization (PSO)

PL

algorytmy ewolucyjne; klastry atomowe; optymalizacja rojem cząstek; potencjał Morsa; potencjał Murrella-Mottrama; system immunologiczny sztuczny

• Wydawca: Wydawnictwo Politechniki Krakowskiej im. Tadeusza Kościuszki
• Czasopismo: Czasopismo Techniczne. Mechanika
• Rocznik: 2010
• Tom: R. 107, z. 4-M
• Strony: 85--97
• Opis fizyczny: Bibliogr. 23 poz.,Rys., wz., tab., wykr.

Twórcy

autor

Mrozek A.

autor

Kuś W.

autor

Burczyński T.

• Instytut Informatyki, Wydział Fizyki, Matematyki i Informatyki, Politechnika Krakowska

Bibliografia

• [1] Ahlrichs R., Elliot S.D., Clusters of Aluminium, a Density Functional Study, Chemical Physics, 1, 1999, 13-21.
• [2] Castro de L.N., Timmis J.J., Artificial Immune Systems as a Novel Soft Computing Paradigm, Soft Computing, 7 (8), 2003, 526-544.
• [3] Castro de L.N., Zuben Vo n F.J., Learning and Optimization Using the Clonal Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6 (3), 2002, 239-251.
• [4] Chan F.T.S., Tiwari M.K. (eds.), Swarm Intelligence. Focus on Ant and Particle Swarm Optimization, I-Tech Education and Publishing, 2007.
• [5] Chou M.Y., Cohen M.L., Electronic Shell Structure in Simple Metal Clusters, Physical Lett. A, 113, 1986, 420-424.
• [6] Cox H., Johnston R.L., Murrell J.N., Modelling of Surface Relaxation and Melting of Aluminium, Surf. Sci., 373, 1997, 67-84.
• [7] Girifalco L.A., Weizer V.G., Application of the Morse Potential Function to Cubic Metals, Physical Review, 114 (3), 1959, 687-690.
• [8] Kennedy J., Eberhart R.C., Shi Y., Swarm Intelligence, Morgan Kaufmann Publishers, 2001.
• [9] Lazinica A. (ed.), Particle Swarm Optimization, In-Tech, 2009.
• [10] Lloyd L.D., Johnston R.L., Modelling aluminium clusters with an empirical many-body potential, Chemical Physics, 236, 1998, 107-121.
• [11] Michalewicz Z., Genetic algorithms + data structures = evolutionary algorithms, Springer-Verlag, Berlin 1996.
• [12] Morse P.M., Diatomic Molecules According to the Wave Mechanics, II, Vibrational levels, Physical Review, 34, 1929, 57-64.
• [13] Mrozek A., Kuś W., Burczyński T., Biological inspired algorithms in modelling of atomic clusters Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems, EUROGEN 2009, Kraków 2009.
• [14] Mrozek A., Kuś W., Orantek P., Burczyński T., Prediction of the aluminium atoms distribution using evolutionary algorithm, Recent Advances in Methods of Artificial Intelligence, Gliwice 2005.
• [15] J.N. Murrell, R.E. Mottram, Potential energy functions for atomic solids, Molecular Physics, 69 (3), 1990, 571-585.
• [16] Murrell J.N., Rodriguez-Ruiz J.A., Potential energy functions for atomic solids. II. Potential functions for diamond-like structures, Molecular Physics, 71 (4), 1990, 823-834.
• [17] Roberts Ch., Johnston R.L., Wilson N.T., A Genetic Algorithm for the Structural Optimization of Morse Clusters, Theoretical Chemistry Accounts, 104, 2000, 123-130.
• [18] Shao X., Cheng L., W. Cai, An Adaptive Immune Optimization Algorithm for Energy minimization Problems, Journal of Chemical Physics, 120 (24), 2004, 11401-11406.
• [19] Wales D.J., Doye J.P.K., 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, 1997, 5111-5116.
• [20] Wales D.J., Scheraga H.A., Global Optimization of Clusters, Crystals and Biomolecules, Science, 285, 1999, 1368-1372.
• [21] Watkins A., Bi X., Phadke A., Parallelizing an Immune-Inspired Algorithm for Efficient Pattern Recognition, Intelligent Engineering Systems through Artificial Neural Networks: Smart Engineering System Design, 13, 2003, 225-230.
• [22] Wierzchoń S.T., Artificial Immune Systems, Theory and Applications, EXIT, Warsaw (in Polish), 2001.
• [23] Zhou J-C., Li W-J., Zhu J-B., Particle Swarm Optimization Computer Simulation of Ni Clusters, Transactions of Nonferrous Metals Society of China, 18, 2008, 410-415.