Advertisement

Particle Swarm Optimization

  • Micael Couceiro
  • Pedram Ghamisi
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Bioinspired algorithms have been employed in situations where conventional optimization techniques cannot find a satisfactory solution, for example, when the function to be optimized is discontinuous, nondifferentiable, and/or presents too many nonlinearly related parameters (Floreano and Mattiussi, Bio-inspired artificial intelligence: Theories, methods, and technologies, 2008). One of the most well-known bioinspired algorithms used in optimization problems is particle swarm optimization (PSO), which basically consists of a machine-learning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. This beginning chapter aims to introduce the main mechanics behind the traditional PSO, outlining its advantages and disadvantages, as well as summarizing the several extensions proposed in the literature over the past almost 20 years.

Keywords

PSO Swarm intelligence Optimization Case studies 

References

  1. Blackwell, T., & Bentley, P. (2002). Don’t push me! Collision-avoiding swarms. Proceedings of the IEEE Congress on Evolutionary Computation (Vol. 2, pp. 1691–1696). Honolulu, HI, USA.Google Scholar
  2. Chia-Feng, J. (2004). A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(2), 997–1006.Google Scholar
  3. Darwin, C. (1872). On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. London: Public Domain Books.Google Scholar
  4. Downey, R. G., & Fellows, M. R. (1999). Parameterized Complexity. Singapore: Springer.Google Scholar
  5. Emery, L., Borland, M., & Shang, H. (2003). Use of a general-purpose optimization module in accelerator control. Proceedings of the Particle Accelerator Conference (PAC), IEEE (Vol. 4, pp. 2330–2332).Google Scholar
  6. Floreano, D., & Mattiussi, C. (2008). Bio-inspired artificial intelligence: Theories, methods, and technologies. Cambridge: MIT Press.Google Scholar
  7. Kannan, S., Slochanal, S., & Padhy, N. (2004). Application of particle swarm optimization technique and its variants to generation expansion problem. Electric Power Systems Research, 70(3), 203–210.CrossRefGoogle Scholar
  8. Kennedy, J., & Eberhart, R. (1995). A new optimizer using particle swarm theory. Proceedings of the IEEE 6th International Symposium on Micro Machine and Human Science (pp. 39–43). Nagoya, Japan.Google Scholar
  9. Krink, T., Vesterstrom, J., & Riget, J. (2002). Particle swarm optimization with spatial particle extension. Proceedings of the IEEE Congress on Evolutionary Computation, 2, 1474–1479.Google Scholar
  10. Lancaster, P., & Salkauskas, K. (1986). Curve and surface fitting. London: Academic Press.Google Scholar
  11. Liu, H., Abraham, A., & Zhang, W. (2007). A fuzzy adaptive turbulent particle swarm optimisation. International Journal of Innovative Computing and Applications, 1(1), 39–47.CrossRefGoogle Scholar
  12. Lovbjerg, M., & Krink, T. (2002). Extending particle swarms with self-organized criticality. Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1588–1593). Honolulu, HI, USA.Google Scholar
  13. Lucey, S., & Matthews, I. (2006). Face refinement through a gradient descent alignment approach. Proceedings of the HCSNet Workshop on Use of Vision in Human-Computer Interaction (pp. 43–49). Australian Computer Society, Inc.Google Scholar
  14. Miranda, V., & Fonseca, N. (2002). New evolutionary particle swarm algorithm (EPSO) applied to voltage/VAR control. Proceedings of the 14th Power Systems Computation Conference (pp. 1–6). Seville, Spain.Google Scholar
  15. Momma, M., & Bennett, K. P. (2002). Pattern search method for model selection of support vector regression. SDM, 132, 261–274.Google Scholar
  16. Pires, E. J., Machado, J. A., Cunha, P. B., & Mendes, L. (2010). Particle swarm optimization with fractional-order velocity. Journal on Nonlinear Dynamics, 61(1–2), 295–301.zbMATHCrossRefGoogle Scholar
  17. Premalatha, K., & Natarajan, A. M. (2009). Hybrid PSO and GA for global maximization. International Journal of Open Problems in Computational Mathematics, 2(4), 597–608.MathSciNetGoogle Scholar
  18. Schaeffer, J., Lu, P., Szafron, D., & Lake, R. (1993). A re-examination of brute-force search. In Proceedings of the AAAI Fall Symposium on Games: Planning and Learning (pp. 51–58).Google Scholar
  19. Secrest, B., & Lamont, G. (2003). Visualizing particle swarm optimization—Gaussian particle swarm optimization. Proceedings of the IEEE Swarm Intelligence Symposium (pp. 198–204). Indianapolis, Indiana, USA.Google Scholar
  20. Shi, Y., & Eberhart, R. (2001). Fuzzy adaptive particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation (Vol. 1, pp. 101–106).Google Scholar
  21. Tillett, J., Rao, T. M., Sahin, F., Rao, R., & Brockport, S. (2005). Darwinian particle swarm optimization. In B. Prasad (Ed.), Proceedings of the 2nd Indian International Conference on Artificial Intelligence (pp. 1474–1487). Pune, India.Google Scholar
  22. Tsai, W. (2002). Social structure of “coopetition” within a multiunit organization: coordination, competition, and intraorganizational knowledge sharing. Organization Science, 13(2), 179–190.CrossRefGoogle Scholar
  23. Valle, Y. D., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 2(2), 171–195.Google Scholar
  24. Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225–239.CrossRefGoogle Scholar
  25. Zhang, W., & Xie, X. (2003). DEPSO: Hybrid particle swarm with differential evolution operator. IEEE International Conference on Systems, Man and Cybernetics, 4, 3816–3821.Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Ingeniarius, LtdMealhadaPortugal
  2. 2.Institute of Systems and Robotics (ISR)University of CoimbraCoimbraPortugal
  3. 3.Faculty of Electrical and Computer EngUniversity of IcelandReykjavikIceland

Personalised recommendations