Abstract
The particle swarm optimization (PSO) algorithm has successfully been applied to dynamic optimization problems with very competitive results. One of its best performing variants is the one based on the atomic model, with quantum and trajectory particles. However, there is no precise knowledge on how these particles contribute to the global behavior of the swarms during the optimization process. This work analyzes several aspects of each type of particle, including the best combination of them for different scenarios, and how many times do they contribute to the swarm’s best. Results show that, for the Moving Peaks Benchmark (MPB), a higher number of trajectory particles than quantum particles is the best strategy. Quantum particles are most helpful immediately after a change in the environment has occurred, while trajectory particles lead the optimization in the final stages. Suggestions on how to use this knowledge for future developments are also provided.
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References
Blackwell, T.: Particle swarm optimization in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, vol. 51, pp. 29–49. Springer, Heidelberg (2007)
Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)
Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10(4), 459–472 (2006)
Blackwell, T.M.: Swarms in dynamic environments. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723. Springer, Heidelberg (2003)
Blackwell, T.M.: Particle swarms and population diversity. Soft Computing 9(11), 793–802 (2005)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Congress on Evolutionary Computation CEC’99, pp. 1875–1882. IEEE, Los Alamitos (1999)
Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Advances in evolutionary computing: theory and applications, pp. 239–262 (2003)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88 (2000)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Li, J.-P., Balazs, M.E., Parks, G.T., Clarkson, P.J.: A species conserving genetic algorithm for multimodal function optimization. Evol. Comput. 10(3), 207–234 (2002)
Parrott, D., Li, X.: A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proceedings of the 2004 Congress on Evolutionary Computation, CEC 2004, vol. 1, pp. 98–103 (2004)
Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Natural Computing 1, 235–306 (2002)
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del Amo, I.G., Pelta, D.A., González, J.R., Novoa, P. (2010). An Analysis of Particle Properties on a Multi-swarm PSO for Dynamic Optimization Problems. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2009. Lecture Notes in Computer Science(), vol 5988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14264-2_4
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DOI: https://doi.org/10.1007/978-3-642-14264-2_4
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