A modified PSO structure resulting in high exploration ability with convergence guaranteed

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 37, Issue 5, 2007, Pages 1271-1289

  Chen, Xin ^a   Li, Yangmin ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Lyapunov theory; Particle swarm optimization with controllable random exploration velocity (PSO CREV); Stochastic approximation; Supermartingale convergence

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CONVERGENCE OF NUMERICAL METHODS; LYAPUNOV METHODS; OPTIMIZATION; RANDOM PROCESSES;

CONTROLLABLE RANDOM EXPLORATION VELOCITY (CREV); LYAPUNOV THEORY; PARTICLE SWARM OPTIMIZATION (PSO); STOCHASTIC APPROXIMATION; SUPERMARTINGALE CONVERGENCE;

BIOMETRICS;

ALGORITHM; ANIMAL; ANIMAL BEHAVIOR; ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; SOCIAL BEHAVIOR; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ANIMALS; BEHAVIOR, ANIMAL; COMPUTER SIMULATION; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MOVEMENT; SOCIAL BEHAVIOR; STOCHASTIC PROCESSES;

EID: 35148840017     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2007.897922     Document Type: Article

References (27)

1. R. C. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proc. 6th Int. Symp. MHS, Nagoya, Japan, 1995, pp. 39-43.
2. J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Netw., Perth, Australia, 1995, pp. 1942-1948.
3. C. F. Juang, "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 2, pp. 997-1006, Apr. 2004.
4. H. Yoshida, K. Kawata, Y. Fukuyama, and Y. Nakanishi, "A particle swarm optimization for reactive power and voltage control considering voltage stability," in Proc. Int. Conf. Intell. Syst. Appl. Power Syst., Rio de Janeiro, Brazil, 1999, pp. 117-121.
5. M. A. Abido, "Particle swarm optimization for multimachine power system stabilizer design," in Proc. Power Eng. Soc. Summer Meeting, 2001, pp. 1346-1351.
6. L. Messerschmidt and A. P. Engelbrecht, "Learning to play games using a PSO-based competitive learning approach," IEEE Trans. Evol. Comput. vol. 8, no. 3, pp. 280-288, Jun. 2004.
7. Y. Li and X. Chen, "Mobile robot navigation using particle swarm optimization and adaptive NN," in Proc. 1st Int. Conf. Nat. Comput. Changsha, China, Lecture Notes in Computer Science, vol. 3612. Berlin, Germany: Springer-Verlag, 2005, pp. 554-559.
8. H. Robbins and S. Monro, "A stochastic approximation method," Ann. Math. Stat., vol. 22, no. 3, pp. 400-407, Sep. 1951.
9. H. J. Kushner and G. G. Yin, Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed. New York: Springer-Verlag, 2003.
10. H. M. Emara and H. A. Fattah, "Continuous swarm optimization technique with stability analysis," in Proc. Amer. Control Conf., 2004, vol. 3, pp. 2811-2817.
11. M. Clerc and J. Kennedy, "The particle swarm: Explosion, stability, and convergence in a multi-dimensional complex space," IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58-73, Feb. 2002.
12. K. Yasuda, A. Ide, and N. Iwasaki, "Adaptive particle swarm optimization," in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2003, pp. 1554-1559.
13. I. C. Trelea, "The particle swarm optimization algorithm: Convergence analysis and parameter selection," Inf. Process. Lett., vol. 85, no. 6, pp. 317-325, 2003.
14. B. Brandstäter and U. Baumgartner, "Particle swarm optimization - Mass-spring system analogon," IEEE Trans. Magn., vol. 38, no. 2, pp. 997-1000, Mar. 2002.
15. Y. Liu, Z. Qin, and Z. Shi, "Hybrid particle swarm optimizer with line search," in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2004, vol. 4, pp. 3751-3755.
16. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240-255, Jun. 2004.
17. R. Poli, C. D. Chio, and W. B. Langdon, "Exploring extended particle swarms: A genetic programming approach," in Proc. Conf. Genet. and Evol. Comput., Washington, DC, 2005, pp. 169-176.
18. K. E. Parsopouls and M. N. Vrahatis, "Recent approaches to global optimization problems through particle swarm optimization," Nat. Comput., vol. 1, no. 2/3, pp. 235-306, Jun. 2002.
19. W. J. Zhang and X. F. Xie, "DEPSO: Hybrid particle swarm with differential evolution operator," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Washington, DC, 2003, pp. 3816-3821.
20. Y. Shi and R. C. Eberhart, "A modified particle swarm optimizer," in Proc. IEEE Int. Conf. Evol. Comput., Anchorage, AK, 1998, pp. 69-73.
21. H. Y. Fan and Y. Shi, "Study on Vmax of particle swarm optimization," in Proc. Workshop Particle Swarm Opt., Indianapolis, IN, 2001.
22. F. van den Bergh, "An analysis of particle swarm optimizers," Ph.D. dissertation, Dept. Comput. Sci., Univ. Pretoria, Pretoria, South Africa, 2002.
23. F. van den Bergh and A. P. Engelbrecht, "A cooperative approach to particle swarm optimization," IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 225-239, Jun. 2004.
24. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, and S. Tiwari, "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization," IIT, Kanpur, India, NTU, Singapore and KanGAL Tech. Rep. 2 005 005, May 2005.
25. Y. Shi and R. C. Eberhart, "Parameter selection in particle swarm optimization," in Proc. 7th Conf. Evol. Program., New York, 1998, pp. 591-600.
26. Y. Shi and R. Eberhart, "An empirical study of particle swarm optimization," in Proc. IEEE Congr. Evol. Comput., Washington, DC, 1999, pp. 1945-1949.
27. R. Mendes, J. Kennedy, and J. Neves, "The fully informed particle swarm: Simple, maybe better," IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 204-210, Jun. 2004.