Particle swarm optimization: An efficient method for tracing periodic orbits in three-dimensional galactic potentials

Monthly Notices of the Royal Astronomical Society

Volumn 359, Issue 1, 2005, Pages 251-260

  Skokos, Ch ^a,b   Parsopoulos, K E ^b   Patsis, P A ^a   Vrahatis, M N ^b  

^a BIOMEDICAL RESEARCH FOUNDATION   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

Author keywords

Galaxies: kinematics and dynamics; Galaxies: structure; Methods: numerical

Indexed keywords

GALAXIES; HAMILTONIANS; ITERATIVE METHODS; NUMERICAL METHODS;

DEFLECTION TECHNIQUES; GALAXIES: KINEMATICS AND DYNAMICS; GALAXIES:STRUCTURE; GLOBAL MINIMIZERS; HAMILTONIAN SYSTEMS; INITIAL CONDITIONS; METHOD: NUMERICAL; PARTICLE SWARM OPTIMIZATION METHOD; PERIODIC ORBITS; THREE-DIMENSIONAL (3D) MODEL;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 18444408420     PISSN: 00358711     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1365-2966.2005.08892.x     Document Type: Article

References (34)

1. Athanassoula E., 1984, Phys. Rep., 114, 319
2. Bonabeau E., Dorigo M., Theraulaz G., 1999, Swarm Intelligence: From Natural to Artificial Systems. Oxford Univ. Press, New York
3. Broucke R., 1969, NASA Techn. Rep., 32, 1360
4. Buta R., 1995, ApJS, 96, 39
5. Clerc M., Kennedy J., 2002, IEEE Trans. Evol. Comput., 6(1), 58
6. Contopoulos G., 1980, A&A, 81, 198
7. Contopoulos G., 2002, Order and Chaos in Dynamical Astronomy. Springer Verlag, Berlin
8. Contopoulos G., Grosbøl P., 1989, A&AR, 1, 261
9. Contopoulos G., Magnenat P., 1985, Celest. Mech., 37, 387
10. Contopoulos G., Papayannopoulos Th., 1980, A&A, 92, 33
11. Eberhart R. C., Kennedy J., 1995, Proc. Sixth Symp. Micro Mach. Hum. Sci., 39
12. Fogel D. B., 2000, IEEE Spectrum, 37(2), 26
13. Hadjidemetriou J., 1975, Celest. Mech., 12, 255
14. Kennedy J., Eberhart R. C., 2001, Swarm Intelligence. Morgan Kaufmann, San Francisco
15. Lieberman M. A., Lichtenberg A. J., 1992, Regular and Chaotic Dynamics. Springer Verlag, New York, p. 17
16. Magoulas G. D., Plagianakos V. P., Vrahatis M. N., 1997, Nonlinear Analysis Theory Meth. & Applic., 30(7), 4545
17. Millonas M. M., 1994, Artificial Life III, 417
18. Miyamoto M., Nagai R., 1975, PASJ, 27, 533
19. Parsopoulos K. E., Vrahatis M. N., 2002, Natural Comp., 1 (2-3), 235
20. Parsopoulos K. E., Vrahatis M. N., 2004, IEEE Transact. Evolutionary Comp., 8 (3), 211
21. Patsis P. A., 2005, MNRAS, 358, 305
22. Patsis P. A., Skokos Ch., Athanassoula E., 2002, MNRAS, 337, 578
23. Patsis P. A., Skokos Ch., Athanassoula E., 2003a, MNRAS, 342, 69
24. Patsis P. A., Skokos Ch., Athanassoula E., 2003b, MNRAS, 346, 1031
25. Pfenniger D., 1984, A&A, 134, 373
26. Pfenniger D., 1996, in Buta R., Crocker D. A., Elmegreen B. G., eds, ASP Conf. Ser. Vol. 91, Barred Galaxies. Astron. Soc. Pac., San Francisco, p. 273
27. Pfenniger D., Friedli D., 1993, A&A, 270, 561
28. Schwefel H.-P., 1994, in Zurada J. M., Marks R. J., II, Robinson C. J., eds, Computational Intelligence: Imitating Life. IEEE Press, New York, p. 116
29. Sellwood J., Wilkinson A., 1993, Rep. Prog. Phys., 56, 173
30. Skokos Ch., 2001, Phys. D, 159, 155
31. Skokos Ch., Patsis P. A., Athanassoula E., 2002a, MNRAS, 333, 847
32. Skokos Ch., Patsis P. A., Athanassoula E., 2002b, MNRAS, 333, 861
33. Storn R., Price K., 1997, J. Global Optimization, 11, 341
34. Trelea I. C., 2003, Inform. Processing Lett., 85, 317