Abstract
Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, we propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.
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References
Wikipedia: Particle swarm optimization. (Wikimedia Foundation), 7 March 2017. https://en.wikipedia.org/wiki/Particle_swarm_optimization. Accessed 8 Apr 2017
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. In: Dorigo, M. (ed.) Swarm Intelligence, vol. 1, no. 1, pp. 33–57, June 2007. https://doi.org/10.1007/s11721-007-0002-0
Pan, F., Hu, X., Eberhart, R., Chen, Y.: An analysis of bare bones particle swarm. In: IEEE Swarm Intelligence Symposium 2008 (SIS 2008), St. Louis, MO, US, pp. 1–5. IEEE, 21 September 2008. https://doi.org/10.1109/SIS.2008.4668301
al-Rifaie, M.M., Blackwell, T.: Bare bones particle swarms with jumps. In: Dorigo, M., et al. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 49–60. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32650-9_5
Sharma, K., Chhamunya, V., Gupta, P.C., Sharma, H., Bansal, J.C.: Fitness based particle swarm optimization. Int. J. Syst. Assur. Eng. Manag. 6(3), 319–329 (2015). https://doi.org/10.1007/s13198-015-0372-4
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Nguyen, L., Amer, A.A., Abdalla, H.I. (2023). A General Framework of Particle Swarm Optimization. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_20
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DOI: https://doi.org/10.1007/978-3-031-18461-1_20
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