Abstract
This paper investigates a Particle Swarm with dynamic topology and a conservation of evaluations strategy. The population is structured on a 2-dimensional grid of nodes, through which the particles interact and move according to simple rules. As a result of this structure, each particle’s neighbourhood degree is time-varying. If at given time step a particle p has no neighbours except itself, p is not evaluated until it establishes at least one link to another particle. A set of experiments demonstrates that the dynamics imposed by the structure provides a consistent and stable behaviour throughout the test set when compared to standard topologies, while the conservation of evaluations significantly reduces the convergence speed of the algorithm. The working mechanisms of the proposed structure are very simple and, except for the size of the grid, they do not require parameters and tuning.
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References
Fernandes, C.M., Laredo, J.L.J., Merelo, J.J., Cotta, C., Nogueras, R., Rosa, A.C.: Performance and scalability of particle Swarms with dynamic and partially connected grid topologies. In: Proceedings of the 5th International Joint Conference on Computational Intelligence IJCCI 2013, pp. 47–55 (2013)
Grassé, P.-P.: La reconstrucion du nid et les coordinations interindividuelles chez bellicositermes et cubitermes sp. La théorie de la stigmergie: Essai d’interpretation du comportement des termites constructeurs, Insectes Sociaux 6, 41–80 (1959)
Hseigh, S.-T., Sun, T.-Y, Liu, C.-C., Tsai, S.-J.: Efficient population utilization strategy for particle swarm optimizers. IEEE Trans. Syst., Man Cybern. Part B 39(2), 444–456 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the IEEE World Congress on Evolutionary Computation, pp. 1671–1676 (2002)
Landa-Becerra, R., Santana-Quintero, L.V., Coello Coello, C.A.: Knowledge incorporation in multi-objective evolutionary algorithms. In: Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, pp. 23–46 (2008)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–296 (2006)
Majercik, S.: GREEN-PSO: conserving function evaluations in particle swarm optimization. In: Proceedings of the IJCCI 2013—International Joint Conference on Computational Intelligence, pp. 160–167 (2013)
Parsopoulos, K.E., Vrahatis, M.N.: UPSO: a unified particle swarm optimization scheme. In: Proceedings of the International Conference of Computational Methods in Sciences and Engineering (ICCMSE 2004), pp. 868–887 (2004)
Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the Swarm Intelligence Symposium SIS’03, pp. 174–181 (2003)
Reyes-Sierra, M., Coello Coello, C.A.: A study of techniques to improve the efficiency of a multiobjective particle swarm optimizer. In: Studies in Computational Intelligence (51), Evolutionary Computation in Dynamic and Uncertain Environments, pp. 269–296 (2007)
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)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Proc. Lett. 85, 317–325 (2003)
Acknowledgments
The first author wishes to thank FCT, Ministério da Ciência e Tecnologia, his Research Fellowship SFRH/BPD/66876/2009. The work was supported by FCT PROJECT [PEst-OE/EEI/LA0009/2013], Spanish Ministry of Science and Innovation projects TIN2011-28627-C04-02 and TIN2011-28627-C04-01, Andalusian Regional Government P08-TIC-03903 and P10-TIC-6083, CEI-BioTIC UGR project CEI2013-P-14, and UL-EvoPerf project.
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Fernandes, C.M., Laredo, J.L.J., Merelo, J.J., Cotta, C., Rosa, A.C. (2016). Particle Swarm Optimization with Dynamic Topology and Conservation of Evaluations. In: Merelo, J.J., Rosa, A., Cadenas, J.M., Dourado, A., Madani, K., Filipe, J. (eds) Computational Intelligence. IJCCI 2014. Studies in Computational Intelligence, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-26393-9_7
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DOI: https://doi.org/10.1007/978-3-319-26393-9_7
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