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Meta-PSO for Multi-Objective EM Problems

  • Chapter
Multi-Objective Swarm Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 261))

Abstract

Recently, the Particle Swarm Optimization (PSO) method has been successfully applied to many different electromagnetic optimization problems. Due to the complex equations, usually calling for a numerical solution, the associated cost function is in general very computationally expensive. A fast convergence of the optimization algorithm is hence of paramount importance to attain results in an acceptable time.

In this chapter few variations over the standard PSO algorithm, referred to as Meta-PSO, aimed to enhance the global search capability, and, therefore, to improve the algorithm convergence, are analyzed.

The Meta-PSO class of methods can be furthermore subdivided into the Undifferentiated and a Differentiated subclasses, whether the law updating particle velocity is the same for all particles or not, respectively.

In recently published open literature the results of the application of the Meta-PSO to the optimization of single-objective problems have been shown. Here we will prove their enhanced properties with respect to standard PSO also for the optimization of multi-objective problems, trough their test in multi-objective benchmarks and multi-objective optimization of an antenna array.

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References

  1. Fonseca, C.M., Fleming, P.J.: Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms Part I and II. IEEE Trans. Syst., Man, Cybern. 28(1), 26–37 (1997)

    Google Scholar 

  2. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)

    MATH  Google Scholar 

  3. Selleri, S., Mussetta, M., Pirinoli, P., Zich, R.E., Matekovits, L.: Some Insight over New Variations of the Particle Swarm Optimization Method. IEEE Antennas and Wireless Propagation Letters 5, 235–238

    Google Scholar 

  4. Reyes-Sierra, M., Coello Coello, C.A.: Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. Int. Journal of Computational Intelligence Research 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  5. Mostaghim, S., Teich, J.: Covering Pareto-optimal Fronts by Subswarms in Multi-objective Particle Swarm Optimization. In: Proc. of Congress on Evolutionary Computation, CEC 2004, vol. 2, June 2004, pp. 1404–1411 (2004)

    Google Scholar 

  6. Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Proc. of 7th Annual Conf. Evol. Program., March 1998, pp. 611–616 (1998)

    Google Scholar 

  7. Hodgson, R.J.W.: Particle swarm optimization applied to the atomic cluster optimization problem. In: Proc. of Genetic and Evolut. Comput. Conf., pp. 68–73 (2002)

    Google Scholar 

  8. Robinson, J., Rahmat-Samii, Y.: Particle swarm optimization in electromagnetics. IEEE Trans. Antennas Propagat. 52, 397–407 (2004)

    Article  MathSciNet  Google Scholar 

  9. Boeringer, D.W., Werner, D.H.: Particle swarm optimization versus genetic algorithms for phased array synthesis. IEEE Trans. Antennas Propagat. 52, 771–779 (2004)

    Article  Google Scholar 

  10. Matekovits, L., Mussetta, M., Pirinoli, P., Selleri, S., Zich, R.E.: Particle swarm optimization of microwave microstrip filters. In: 2004 IEEE AP-S Symposium Digests, Monterey (CA), June 20-26 (2004)

    Google Scholar 

  11. Gies, D., Rahmat-Samii, Y.: Reconfigurable array design using parallel particle swarm optimization. In: IEEE AP-S Symposium Digests, June 22-27, 2003, pp. 177–180 (2003)

    Google Scholar 

  12. Ciuprina, G., Ioan, D., Munteanu, I.: Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans. on Magnetics 38, 1037–1040 (2002)

    Article  Google Scholar 

  13. Matekovits, L., Mussetta, M., Pirinoli, P., Selleri, S., Zich, R.E.: Improved PSO Algorithms for Electromagnetic Optimization. In: 2005 IEEE AP-S Symposium Digests, Washington (DC), July 3-8 (2005)

    Google Scholar 

  14. Jin, N., Rahmat-Samii, Y.: Parallel particle swarm optimization and finite- difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs. IEEE Trans. Antennas Propagat. 53, 3459–3468 (2005)

    Article  Google Scholar 

  15. Cui, S., Weile, D.S.: Application of a parallel particle swarm optimization scheme to the design of electromagnetic absorbers. IEEE Trans. Antennas Propagat. 53, 3616–3624 (2005)

    Article  Google Scholar 

  16. Jin, N., Rahmat-Samii, Y.: IEEE Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations. IEEE Trans. Antennas Propagat. 55, 556–567 (2007)

    Article  Google Scholar 

  17. Moradi, A., Fotuhi-Firuzabad, M.: Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm. IEEE Trans. Power Deliv. 23, 271–279 (2008)

    Article  Google Scholar 

  18. Selleri, S., Mussetta, M., Pirinoli, P., Zich, R.E., Matekovits, L.: Differentiated Meta-PSO Methods for Array Optimozation. IEEE Trans. Antennas Propagat. 56 (January 2008)

    Google Scholar 

  19. Parsopoulos, K., Vrahatis, M.: Recent approaches to global optimization problems through particle swarm optimization. Natural Computing 1, 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  20. Velduizen, D., Zydallis, J., Lamont, G.: Considerations in engineering parallel multiobjective evolutionary optimizations. IEEE Trans. Evol. Comput. 7(2), 144–173 (2003)

    Article  Google Scholar 

  21. Genovesi, S., Monorchio, A., Mittra, R., Manara, G.: A Sub-boundary Approach for Enhanced Particle Swarm Optimization and its Application to the Design of Artificial Magnetic Conductors. IEEE Trans. Antennas Propagat. 55, 766–770 (2007)

    Article  Google Scholar 

  22. Kennedy, J.: Stereotyping: improving particle swarm performance with cluster analysis. In: Proc. of Congress on Evolutionary Computation, Washington DC, July 6-9, vol. 3, pp. 1931–1938 (1999)

    Google Scholar 

  23. Shi, Y., Krohling, R.A.: Co-evolutionary particle swarm optimization to solve min-max problems. In: Proc. of Congress on Evolutionary Computation, Honolulu, HI, May 12-17, vol. 2, pp. 1682–1687 (2002)

    Google Scholar 

  24. van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Tans. Evol. Comput. 8, 225–239 (2004)

    Article  Google Scholar 

  25. Kennedy, J.: Small worlds and mega-minds: effect of neighborhood topology on particle swarm performance. In: Proc. of Congress on Evolutionary Computation, Washington DC, July 6-9, vol. 3, pp. 1931–1938 (1999)

    Google Scholar 

  26. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  27. Eberhart, R.C., Shi, Y.: Particle swarm optimisation: developments, applications and resources. In: Proc. of Congress on Evolutionary Computation, pp. 81–86 (2001)

    Google Scholar 

  28. Bajpai, P., Singh, S.N.: Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market. IEEE Trans. Power Syst. 22, 2152–2160 (2007)

    Article  Google Scholar 

  29. Xu, S., Rahmat-Samii, Y.: Boundary Conditions in Particle Swarm Optimization Revisited. IEEE Trans. Antennas Propagat. 55, 760–765 (2007)

    Article  Google Scholar 

  30. Mikki, S.M., Kishk, A.A.: Hybrid Periodic Boundary Condition for Particle Swarm Optimization. IEEE Trans. Antennas Propagat. 55, 3251–3256 (2007)

    Article  MathSciNet  Google Scholar 

  31. Mansour, M.M., Mekhamer, S.F., El-Sherif El-Kharbawe, N.: A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays. IEEE Trans. Power Deliv. 22, 1400–1410 (2007)

    Article  Google Scholar 

  32. Mussetta, M., Selleri, S., Pirinoli, P., Zich, R., Matekovits, L.: Improved Particle Swarm Optimization algorithms for electromagnetic optimization. Journal of Intelligent and Fuzzy Systems 19, 75–84 (2008)

    MATH  Google Scholar 

  33. Olcan, D.I., Kolundzija, B.M.: Adaptive random search for antenna optimization. In: IEEE Proc. Antennas and Propagation Society International Symposium, June 2004, vol. 1, pp. 1114–1117 (2004)

    Google Scholar 

  34. Jin, N., Rahmat-Samii, Y.: Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations. IEEE Trans. Antennas and Propagation 5(3), 556–567 (2007)

    Article  Google Scholar 

  35. Mussetta, M., Pirinoli, P., Selleri, S., Zich, R.E.: Differentiated Meta-PSO Techniques for Antenna Optimization. In: Proc. of ICEAA, Turin, Italy, September 2007, vol. 53, pp. 2674–2679 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129, Torino, Italy

    Marco Mussetta & Paola Pirinoli

  2. Department of Electronics and Telecommunications, University of Florence, Via C. Lombroso 6/17, I-50134, Florence, Italy

    Stefano Selleri

  3. Department of Energy, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milano, Italy

    Marco Mussetta & Riccardo E. Zich

Authors
  1. Marco Mussetta
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  2. Paola Pirinoli
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  3. Stefano Selleri
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  4. Riccardo E. Zich
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Editor information

Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Estado do Rio de Janeiro, sala 5022-D Rua São Francisco Xavier 524, 20550-900, MARACANÃ, RJ, Brazil

    Nadia Nedjah  & Luiza de Macedo Mourelle  & 

  2. Universidade Federal do Paraná Departamento de Engenharia Elétrica Pós-Graduação em Engenharia Elétrica Centro Politécnico 81531-980, Curitiba-PR, Brazil and Pontifícia Universidade Católica do Paraná Centro, Brazil

    Leandro dos Santos Coelho

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Mussetta, M., Pirinoli, P., Selleri, S., Zich, R.E. (2010). Meta-PSO for Multi-Objective EM Problems. In: Nedjah, N., dos Santos Coelho, L., de Macedo Mourelle, L. (eds) Multi-Objective Swarm Intelligent Systems. Studies in Computational Intelligence, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05165-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-05165-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05164-7

  • Online ISBN: 978-3-642-05165-4

  • eBook Packages: EngineeringEngineering (R0)

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