Abstract
This paper uses the emergent properties of a Self-Organized Criticality (SOC) system for controlling the inertia weight of the Particle Swarm Optimization (PSO) algorithm. The strategy is based on the SOC Bak-Sneppen model of co-evolution. In this model, an ecosystem is simulated by a population of species with random fitness connected in a ring topology. In each time-step, the worst species and its neighbors are randomly mutated. The threshold fitness of the model, which is the highest level the lowest fitness has reached, is used in this paper for controlling the inertia weight. The resulting algorithm is named Bak-Sneppen threshold PSO (BSt-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 parameters. The results demonstrate that the BSt-PSO is clearly faster than the other algorithms in meeting the convergence criteria.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arumugam, M.S., Rao, M.V.C.: 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 2006, Article ID 79295, 17 pages (2006)
Bak, P., Tang, C., Wiesenfeld, K.: Self-organized Criticality: an Explanation of 1/f Noise. Physical Review of Letters 59(4), 381–384 (1987)
Bak, P., Sneppen, K.: Punctuated Equilibrium and Criticality in a Simple Model of Evolution. Physical Review of Letters 71(24), 4083–4086 (1993)
Boettcher, S., Percus, A.G.: Optimization with Extremal Dynamics. Complexity 8(2), 57–62 (2003)
Eberhart, R.C., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 84–88. IEEE Press (2000)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation 3(2), 124–141 (1999)
Fernandes, C.M., Merelo, J.J., Ramos, V., Rosa, A.C.: A Self-Organized Criticality Mutation Operator for Dynamic Optimization Problems. In: Proceedings of the 2008 Genetic and Evolutionary Computation Conference, pp. 937–944. ACM (2008)
Fernandes, C.M., Laredo, J.L.J., Mora, A.M., Rosa, A.C., Merelo, J.J.: A Study on the Mutation Rates of a Genetic Algorithm Interacting with a Sandpile. In: Di Chio, C., et al. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 32–42. Springer, Heidelberg (2011)
Fernandes, C.M., Merelo, J.J., Rosa, A.C.: Using Self-Organized Criticality for Adjusting the Parameters of a Particle Swarm Optimization Algorithm. In: Proceedings of the 4th International Conference on Evolutionary Computation Theory and Applications (2012)
Grefenstette, J.J.: Genetic Algorithms for Changing Environments. In: Proceedings of Parallel Problem Solving from Nature II, pp. 137–144. North-Holland, Amsterdam (1992)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the IEEE World Congress on Evolutionary Computation, pp. 1671–1676 (2002)
Krink, T., Rickers, P., René, T.: Applying Self-organized Criticality to Evolutionary Algorithms. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 375–384. Springer, Heidelberg (2000)
Krink, T., Thomsen, R.: Self-Organized Criticality and Mass Extinction in Evolutionary Algorithms. In: Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC 2001), vol. 2, pp. 1155–1161. IEEE Press (2001)
Løvbjerg, M., Krink, T.: Extending particle swarm optimizers with self-organized criticality. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1588–1593. IEEE Computer Society (2002)
Ratnaweera, A., Halgamuge, K.S., Watson, H.C.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation 8(3), 240–254 (2004)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of IEEE 1998 International Conference on Evolutionary Computation, pp. 69–73. IEEE Press (1998)
Shi, Y., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. In: Proceedings of the 1999 IEEE Int. Congr. Evolutionary Computation, vol. 3, pp. 101–106 (1999)
Suresh, K., Ghosh, S., Kundu, D., Sen, A., Das, S., Abraham, A.: Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search. In: Proceedings of the 8th Inter. Conference on Intelligent Systems Design and Applications, vol. 2, pp. 253–258. IEEE, Washington, DC (2008)
Tinós, R., Yang, S.: A self-organizing Random Immigrants Genetic Algorithm for Dynamic Optimization Problems. Genetic Programming and Evolvable Machines 8(3), 255–286 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fernandes, C.M., Merelo, J.J., Rosa, A.C. (2015). A Time-Varying Inertia Weight Strategy for Particles Swarms Based on Self-Organized Criticality. In: Madani, K., Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2012. Studies in Computational Intelligence, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-11271-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-11271-8_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11270-1
Online ISBN: 978-3-319-11271-8
eBook Packages: EngineeringEngineering (R0)