Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Coupling Particles Swarm Optimization for Multimodal Electromagnetic Problems

  • Pham, Minh-Trien (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Song, Min-Ho (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Koh, Chang-Seop (School of Electrical & Computer Engineering, Chungbuk National University)
  • Received : 2009.06.23
  • Accepted : 2010.05.06
  • Published : 2010.09.01
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Abstract

Particle swarm optimization (PSO) algorithm is designed to find a single global optimal point. However, the PSO needs to be modified in order to find multiple optimal points of a multimodal function. These modifications usually divide a swarm of particles into multiple subswarms; in turn, these subswarms try to find their own optimal point, resulting in multiple optimal points. In this work, we present a new PSO algorithm, called coupling PSO to find multiple optimal points of a multimodal function based on coupling particles. In the coupling PSO, each main particle may generate a new particle to form a couple, after which the couple searches its own optimal point using non-stop-moving PSO algorithm. We tested the suggested algorithm and other ones, such as clustering PSO and niche PSO, over three analytic functions. The coupling PSO algorithm was also applied to solve a significant benchmark problem, the TEAM workshop problem 22.

Keywords

References

  1. A. Passaro and A. Starita, “Clustering particles formultimodal function optimization,” Proceedings ofECAI Workshop on Evolutionary Computation, Rivadel Garda, Italy, August 2006.
  2. R. Brits, A. P. Engelbrecht, and F. van den Bergh,“Scalability of niche PSO,” Proceedings of the IEEESwarm Intelligence Symposium (SIS '03), pp. 228-234,Indianapolis, IN, USA, April 2003.
  3. J. H. Seo, C. H. Im, S. Y. Kwak, C. G. Lee, H. K.Jung, “An improved PSO algorithm mimicking territorialdispute between groups for multimodal functionoptimization problems,” IEEE Trans. on Magnetics,Vol. 44, No. 6, pp. 1046-1049, 2008. https://doi.org/10.1109/TMAG.2007.914855
  4. B. Brandstaetter, “SMES optimization benchmark,TEAM Problem 22, 3 parameter problem,”http://www.igte.tu-graz.ac.at/archive/team_new/team3.php.
  5. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,”Proceedings of IEEE International Conferenceon Neural Networks (ICNN ’95), Vol. 4, pp.1942-1948, IEEE Service Center, Perth, Western Australia,November-December 1995. https://doi.org/10.1109/ICNN.1995.488968
  6. Y. Shi, R.C. Eberhart, “A modified particle swarmoptimizer,” Proceedings of the IEEE World Conferenceon Computational Intelligence, pp. 69-73, Anchorage,Alaska, May 1998.
  7. J. Kennedy, “The particle swarm: social adaptation ofknowledge,” Proceedings of IEEE Congress on Evolutionary Computation (CEC ’97), pp. 303-308, Indianapolis,IN, USA, April 1997.
  8. J. Kennedy and R. Mendes. “Neighborhood topologiesin fully informed and best-of-neighborhood particleswarms,” IEEE Transactions on Systems, Man,and Cybernetics, Part C, 36(4): pp. 515-519, 2006. https://doi.org/10.1109/TSMCC.2006.875410
  9. R. C. Eberhart and J. Kennedy, “A new optimizerusing particle swarm theory,” Proceedings of the 6thIEEE International Symposium on Micro Machineand Human Science (MHS '95), pp. 39-43, Nagoya,Japan, October 1995.
  10. J. Kennedy, R. Mendes, “Population structure andparticle swarm performance,” Proceedings of theEvolutionary Computation on 2002. CEC '02. Proceedingsof the 2002 Congress, pp. 1671-1676, May12-17, 2002.
  11. R. Brits, A. P. Engelbrecht, and F. van den Bergh,“Solving systems of unconstrained equations usingparticle swarm optimization,” Proceedings of theIEEE International Conference on Systems, Man andCybernetics (SMC ’02), Vol. 3, pp. 100-105, Hammamet,Tunisia, October 2002.
  12. R. Brits, A. P. Engelbrecht, and F. van den Bergh, “Aniching particle swarm optimizer,” Proceedings ofthe 4th Asia-Pacific Conference on Simulated Evolutionand Learning (SEAl ’02), Vol. 2, pp. 692-696,Singapore, November 2002.
  13. E. Thie'mard, “Economic Generation of Low-Discrepancy Sequences with a b-ary Gray Code,” Department of Mathematics, Ecole Polytechnique Fe´ de´rale de Lausanne, Lausanne, Switzerland.
  14. J. Kennedy, “Small worlds and mega-minds: effectsof neighborhood topology on particle swarm performance,”Proceedings of the IEEE Congress onEvolutionary Computation, pp. 1931-1938, July 1999.
  15. F. van den Bergh and A. P. Engelbrecht, “A new locallyconvergent particle swarm optimizer,” Proceedingsof the IEEE International Conference on Systems,Man and Cybernetics (SMC ’02), Vol. 3, pp. 96-101,Hammamet, Tunisia, October 2002.
  16. F. van den Bergh, “An Analysis of Particle SwarmOptimizers.” Ph.D. thesis, Department of ComputerScience, University of Pretoria, Pretoria, South Africa,2002.
  17. F. van den Bergh, A.P. Engelbrecht, “A study of particleswarm optimization particle trajectories,” InformationScience, pp. 937-971, August 2006.

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