Abstract
Inspired by the group discussion behavior of students in class, a new group topology is designed and incorporated into original particle swarm optimization (PSO). And thus, a novel modified PSO, called group discussion mechanism based particle swarm optimization (GDPSO), is proposed. Using a group discussion mechanism, GDPSO divides a swarm into several groups for local search, in which some smaller teams with a dynamic change topology are included. Particles with the best fitness value in each group will be selected to learn from each other for global search. To evaluate the performance of GDPSO, four benchmark functions are selected as test functions. In the simulation studies, the performance of GDPSO is compared with some variants of PSOs, including the standard PSO (SPSO), PSO-Ring and PSO-Square. The results confirm the effectiveness of GDPSO in some of the benchmarks.
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References
Eberchart, R.C., Kennedy, J.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, Australia (1995)
Eberchart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Clerc, M., Kennedy, J.: The particle swarm: explosion, stability, and convergence in multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1927–1930 (1999)
Suganthan, P.N.: Particle swarm optimizer with neighborhood operator. In: Proceedings of the IEEE Congress of Evolutionary Computation, pp. 1958–1961 (1999)
Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, pp. 1931–1938 (1999)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8, 204–210 (2004)
Jiang, B., Wang, N., Wang, L.: Particle swarm optimization with age-group topology for multimodal functions and data clustering. Commun. Nonlinear Sci. Numer. Simul. 18, 3134–3145 (2013)
Wei, H.L., Isa, N.A.M.: Particle swarm optimization with increasing topology connectivity. Eng. Appl. Artif. Intell. 27, 80–102 (2014)
Angeline, P.J.: Using selection to improve particle swarm optimization. In: Proceedings of IEEE World Congress on Computational Intelligence, Anchorage, Alaska, pp. 84–89 (1998)
Li, L.L., Wang, L., Liu, L.H.: An effective hybrid PSOSA strategy for optimization and its application to parameter estimation. Appl. Math. Comput. 179, 135–146 (2006)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1671–1676 (2002)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of IEEE World Congress on Computational Intelligence Evolutionary Computation (1998)
Acknowledgments
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71001072, 71271140, 71471158, 71501132, 2016A030310067) and the Natural Science Foundation of Guangdong Province (Grant no. 2016A030310074).
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Tan, L.J., Liu, J., Yi, W.J. (2016). Group Discussion Mechanism Based Particle Swarm Optimization. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_9
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DOI: https://doi.org/10.1007/978-3-319-42297-8_9
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