Using Self-organized Criticality for Adjusting the Parameters of a Particle Swarm

Carlos M. Fernandes, Juan Julián Merelo, Agostinho C. Rosa

2012

Abstract

The local and global behavior of Self-Organized Criticality (SOC) systems may be an efficient source for controlling the parameters of a Particle Swarm Optimization (PSO) without hand-tuning. This paper proposes a strategy based on the SOC Bak-Sneppen model of co-evolution for adjusting the inertia weight and the acceleration coefficients values of the PSO. In order to increase exploration, the model is also used to perturb the position of the particles. The resulting algorithm is named Bak-Sneppen PSO (BS-PSO). An experimental setup compares the new algorithm with versions of the PSO with varying inertia weight, including a state-of-the-art algorithm with dynamic variation of the weight value and perturbation of the particles’ positions. The parameter values generated by the model are investigated in order to understand the dynamic of the algorithm and explain its performance.

References

  1. Arumugam, M. S., Rao, M. V. C., 2006. On the Performance of the Particle Swarm Optimization Algorithm with Various Inertia Weight Variants for Computing Optimal Control of a Class of Hybrid Systems. Discrete Dynamics in Nature and Society, vol. 2006, Article ID 79295, 17 pages.
  2. Bak, P., Tang, C., Wiesenfeld, K., 1987. Self-organized Criticality: an Explanation of 1/f Noise. Physical Review of Letters, Vol. 59(4), 381-384.
  3. Bak, P., and Sneppen, K., 1993. Punctuated Equilibrium and Criticality in a Simple Model of Evolution. Physical Review of Letters, Vol. 71(24), 4083-4086.
  4. Boettcher, S., Percus, A. G., 2003. Optimization with Extremal Dynamics. Complexity, Vol. 8(2), pp. 57-62, 2003.
  5. Eberhart, R. C., Shi, Y., 2000. Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In Proceedings of the 2000 Congress on Evolutionary Computation, IEEE Press, 84-88.
  6. Eiben, A. E., Hinterding, R., Michalewicz, Z. 1999. Parameter Control in Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation, 3(2), 124-141.
  7. Fernandes, C. M., Merelo, J. J., Ramos, V., Rosa, A. C. 2008. A Self-Organized Criticality Mutation Operator for Dynamic Optimization Problems. In Proceedings of the 2008 Genetic and Evolutionary Computation Conference, ACM, 937-944.
  8. Fernandes, C. M., Laredo, J. L. J., Mora, A. M., Rosa, A. C., Merelo, J. J., 2011. A Study on the Mutation Rates of a Genetic Algorithm Interacting with a Sandpile. In Proc. of the 2011 International Conference on Applications of Evolutionary Computation I, C. Di Chio et al. (Eds.), Springer-Verlag,, 32-42.
  9. Grefenstette, J. J., 1992. Genetic Algorithms for Changing Environments. In Proceedings of Parallel Problem Solving from Nature II, North-Holland, Amsterdam, 137-144.
  10. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, Vol.4, 1942-1948.
  11. Kennedy, J., Eberhart., R. C., 2001. Swarm Intelligence. Morgan Kaufmann, San Francisco.
  12. Krink, T., Rickers, P., René, T., 2000. Applying Selforganized Criticality to Evolutionary Algorithms. In Proceedings of the 6th International Conference on Parallel Problem Solving from Nature (PPSN-VI), LNCS 1917, Springer, 375-384.
  13. Krink, T., Thomsen, R., 2001. Self-Organized Criticality and Mass Extinction in Evolutionary Algorithms. In Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC'2001), Vol. 2, IEEE Press, 1155-1161.
  14. Løvbjerg, M., Krink, T., 2002. Extending particle swarm optimizers with self-organized criticality. In Proceedings of the 2002 IEEE Congress on Evolutionary Computation, Vol. 2, IEEE Computer Society, 1588-1593.
  15. Ratnaweera, A., Halgamuge, K. S., and Watson, H. C., 2004. Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation, Vol. 8(3), 240-254.
  16. Shi, Y. Eberhart, R. C., 1998. A Modified Particle Swarm Optimizer. In Proceedings of IEEE 1998 International Conference on Evolutionary Computation, IEEE Press, 69-73.
  17. Shi, Y. Eberhart, R. C., 1999. Empirical Study of Particle Swarm Optimization. In Proceedings of the 1999 IEEE Int. Congr. Evolutionary Computation, vol. 3, 1999, 101-106.
  18. Suresh, K., Ghosh, S., Kundu, D., Sen, A., Das, S., Abraham, A., 2008. Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search. In Proceedings of the 8th Inter. Conference on Intelligent Systems Design and Applications, Vol. 2. IEEE, Washington, DC, USA, 253-258.
  19. Tinós, R., Yang, S., 2007. A self-organizing Random Immigrants Genetic Algorithm for Dynamic Optimization Problems. Genetic Programming and Evolvable Machines, Vol. 8(3), 255-286.
Download


Paper Citation


in Harvard Style

M. Fernandes C., Merelo J. and C. Rosa A. (2012). Using Self-organized Criticality for Adjusting the Parameters of a Particle Swarm . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 62-71. DOI: 10.5220/0004158200620071


in Bibtex Style

@conference{ecta12,
author={Carlos M. Fernandes and Juan Julián Merelo and Agostinho C. Rosa},
title={Using Self-organized Criticality for Adjusting the Parameters of a Particle Swarm},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={62-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004158200620071},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - Using Self-organized Criticality for Adjusting the Parameters of a Particle Swarm
SN - 978-989-8565-33-4
AU - M. Fernandes C.
AU - Merelo J.
AU - C. Rosa A.
PY - 2012
SP - 62
EP - 71
DO - 10.5220/0004158200620071