Abstract
This paper proposes an asynchronous and steady state update strategy for the Particle Swarm Optimization (PSO) inspired by the Bak-Sneppen model of co-evolution. The model consists of a set of fitness values (representing species) arranged in a network. By replacing iteratively the least fit species and its neighbors with random values (simulating extinction), the average fitness of the population tends to grow while the system is driven to a critical state. Based on these rules, we implement a PSO in which only the worst particle and its neighbors are updated and evaluated in each time-step. The other particles remain steady during one or more iterations, until they eventually meet the update criterion. The steady state PSO (SS-PSO) was tested on a set of benchmark functions, with three different population structures: lbest ring and lattice with von Neumann and Moore neighborhood. The experiments demonstrate that the strategy significantly improves the quality of results and convergence speed with Moore neighborhood. Further tests show that the major factor of enhancement is the selective pressure on the worst, since replacing the best or a random particle (and neighbors) yields inferior results.
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Aziz, N.A.B., Mubin, M., Mohamad, M.S., Aziz, K.A.: A synchronous-asynchronous particle swarm optimisation algorithm. Sci. World J. 2014, 1–17 (2014). Article ID 123019
Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: an explanation of 1/f noise. Phys. Rev. Lett. 59(4), 381–384 (1987)
Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Phys. Rev. Lett. 71(24), 4083–4086 (1993)
Boettcher, S., Percus, A.G.: Optimization with extremal dynamics. Complexity 8(2), 57–62 (2003)
Carlisle, A., Dozier, G.: An off-the-shelf PSO. In: Workshop on Particle Swarm Optimization (2001)
Chen, M.-R., Li, X., Lu, Y.-Z.: A novel particle swarm optimizer hybridized with extremal optimization. App. Soft Comput. 10(2), 367–373 (2010)
Fernandes, C.M., Merelo, J.J., Rosa, A.C.: Controlling the parameters of the particle swarm optimization with a self-organized criticality model. In: Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 153–163. Springer, Heidelberg (2012)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the IEEE World Congress Evolutionary Computation, pp. 1671–1676 (2002)
Koh, B.-I., George, A.D., Haftka, R.T., Fregly, B.J.: Parallel asynchronous particle swarm optimization. Int. J. Numer. Meth. Eng. 67(4), 578–595 (2006)
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)
Luo, J., Zhang, Z.: Research on the parallel simulation of asynchronous pattern of particle swarm optimization. Comput. Simul. 22(6), 78–170 (2006)
Majercik, S.: GREEN-PSO: conserving function evaluations in particle swarm optimization. In: Proceedings of the IJCCI 2013, pp. 160–167 (2013)
McNabb, A.: Serial PSO results are irrelevant in a multi-core parallel world. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 3143–3150 (2014)
Perez, R., Basterrechea, J.: Particle swarm optimization and its application to antenna farfield-pattern prediction from planar scanning. Microw. Opt. Technol. Lett. 44(5), 398–403 (2005)
Rada-Vilela, J., Zhang, M., Seah, W.: A performance study on synchronous and asynchrounous updates in particle swarm. Soft. Comput. 17(6), 1019–1030 (2013)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE (1998)
Venter, G., Sobieszczanski-Sobieski, J.: A parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J. Aerosp. Comput. Inf. Commun. 3(3), 123–137 (2006)
Acknowledgements
First author wishes to thank FCT, Ministério da Ciência e Tecnologia, his Research Fellowship SFRH/BPD/66876/2009). This work was supported by FCT PROJECT [UID/EEA/50009/2013], EPHEMECH (TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity), PROY-PP2015-06 (Plan Propio 2015 UGR), project CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada.
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Fernandes, C.M., Merelo, J.J., Rosa, A.C. (2016). An Asynchronous and Steady State Update Strategy for the Particle Swarm Optimization Algorithm. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_16
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