Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Comparative Study on Dimensionality and Characteristic of PSO

PSO의 특징과 차원성에 관한 비교연구

  • Published : 2006.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

Keywords

References

  1. D. E. Goldberg, Genetic Algorithms in search, Optimization & Machine Learning, Addison-wesley, 1989
  2. K. A. De Jong, 'Are genetic algorithms function optimizers?', In R. Manner and B. Manderick, editors, Parallel Problem Solving from Nature 2, North-Holland, Amsterdam, 1992
  3. Z. Michalewicz, Genetic Algorithms+Data Structure=Evolution Programs, Springer-Verlag, 1992
  4. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press, 1992
  5. H. G. Beyer, The Theory of Evolution Strategies, Berlin, Germany: Springer-Verlag, 2001
  6. H. G. Beyer and H. P. Schwefel, Evolution strategies: A comprehensive introduction, Nat. Comput., vol. 1, no. 1, pp. 3-52, 2002 https://doi.org/10.1023/A:1015059928466
  7. D. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Piscataway, NJ: IEEE Press, 1996
  8. J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks, vol. IV, pp. 1942-1948, 1995 https://doi.org/10.1109/ICNN.1995.488968
  9. M. A. Abido, Optimal Design of Power-System Stabilizers Using Particle Swarm Optimization, IEEE Trans. Energy Conversion, vol. 17, no. 3, pp. 406-413, 2002 https://doi.org/10.1109/TEC.2002.801992
  10. Z. L. Gaing, A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System, IEEE Trans. Energy Conversion, vol. 19, no. 2, pp. 384-391, 2004 https://doi.org/10.1109/TEC.2003.821821
  11. K. E. Parsopoulos and M. N. Vrahatis, On the Computation of All Global Minimizers Through Particle Swarm Optimization, IEEE Trans. Evolutionary Computation, vol. 8, no. 3, pp. 211-224, 2004 https://doi.org/10.1109/TEVC.2004.826076
  12. J. Robinson and Y. Rahmat-Samii, Particle Swarm Optimization in Electromagnetics, IEEE Trans. Antennas and Propagation, vol. 52, no. 2, pp. 397-407, 2004 https://doi.org/10.1109/TAP.2004.823969
  13. A. Salman, I. Ahmad, and S. Al-Madani, Particle swarm optimization for task assignment problem, Microprocessors and Microsystems, vol. 26, no., pp. 363-371, 2002 https://doi.org/10.1016/S0141-9331(02)00053-4
  14. J. Kennedy, The particle swarm: Social adaptation of knowledge, Proc. IEEE Int. Conf. Evolutionary Comput. , pp. 303-308, 1997 https://doi.org/10.1109/ICEC.1997.592326
  15. S. Mostaghim and J. Teich, Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO), Proc. IEEE 2003 Swarm Intelligence Symp., pp. 26-3, 2003 https://doi.org/10.1109/SIS.2003.1202243
  16. E. C. Laskari, K. E. Parsopoulos, and M. N. Vrahatis, Particle swarm optimization for minimax problems, Proc. IEEE 2002 Congr. Evolutionary Computation, pp. 1582-1587, 2002 https://doi.org/10.1109/CEC.2002.1004478
  17. E. C. Laskari, K. E. Parsopoulos, and M. N. Vrahatis, Particle swarm optimization for integer programming, Proc. IEEE 2002 Congr. Evolutionary Computation, pp. 1576-1581, 2002 https://doi.org/10.1109/CEC.2002.1004477
  18. R Eberhart and Y. Shi, Comparison between genetic algorithms and particle swarm optimization, LNCS-Evolutionary Programming VII, vol. 1447, pp. 611-616, 1998 https://doi.org/10.1007/BFb0040812
  19. B. Liu, L. Wang, Y. H. lin, F. Tang and D. X. Huang, Improved particle swarm optimization combined with chaos, Chaos, Solitons & Fractals, vol. 25, no. 5, pp. 1261-1271, 2005 https://doi.org/10.1016/j.chaos.2004.11.095
  20. X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, and L. M. Wang, An improved GA and a novel PSQ-GA-based hybrid algorithm, Information Processing Letters, vol. 93, pp. 255-261, 2005 https://doi.org/10.1016/j.ipl.2004.11.003
  21. B. Brandstatter and U. Baumgartner, Particle Swarm Optimization-Mass-Spring System Analogon, IEEE Trans. Magnetics, vol. 38, no. 2, pp. 997-1000, 2002 https://doi.org/10.1109/20.996256
  22. S. He, Q. H. Wu, J. Y. Wen, J. R. Saunders, and R. C. Paton, A particle swarm optimizer with passive congregation, Bio-Systems, vol. 78, pp. 135-147, 2004 https://doi.org/10.1016/j.biosystems.2004.08.003

Cited by

  1. Evolutionary Design of Radial Basis Function-based Polynomial Neural Network with the aid of Information Granulation vol.60, pp.4, 2011, https://doi.org/10.5370/KIEE.2011.60.4.862
  2. A new approach to radial basis function-based polynomial neural networks: analysis and design vol.36, pp.1, 2013, https://doi.org/10.1007/s10115-012-0551-4