# Evaluation of selected fuzzy particle swarm optimization algorithms

## Tomasz Krzeszowski, Krzysztof Wiktorowicz

DOI: http://dx.doi.org/10.15439/2016F206

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 571–575 (2016)

Abstract. This paper is devoted to an evaluation of selected fuzzy particle swarm optimization algorithms. Two non-fuzzy and four fuzzy algorithms are considered. The Takagi-Sugeno fuzzy system is utilized to change the parameters of these algorithms. A modified fuzzy particle swarm optimization method is proposed, in which each of the particles has its own inertia weight and coefficients of the cognitive and social components. The evaluation is based on the common nonlinear benchmark functions used for testing optimization methods. The ratings of the algorithms are assigned on the basis of the mean of the objective function and the relative success.

### References

- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. of IEEE Int. Conf. on Neural Networks, vol. 4. IEEE Press, Piscataway, NJ, 1995, pp. 1942–1948.
- A. Alfi and M.-M. Fateh, “Intelligent identification and control using improved fuzzy particle swarm optimization,” Expert Systems with Applications, vol. 38, no. 10, pp. 12 312–12 317, 2011.
- T. Niknam, “A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem,” Applied Energy, vol. 87, no. 1, pp. 327–339, 2010.
- S. Saini, N. Zakaria, D. R. A. Rambli, and S. Sulaiman, “Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization,” PLoS ONE, vol. 10, no. 5, 2015.
- M. Adamczyk, “Parallel feature selection algorithm based on rough sets and particle swarm optimization,” in Computer Science and Information Systems (FedCSIS), 2014 Federated Conf. on, Sept 2014, pp. 43–50.
- D. Srinivasan, W. H. Loo, and R. L. Cheu, “Traffic incident detection using particle swarm optimization,” in Swarm Intelligence Symposium. SIS ’03. Proceedings of the IEEE, April 2003, pp. 144–151.
- A. Karami and M. Guerrero-Zapata, “A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks,” Neurocomputing, vol. 149, Part C, pp. 1253–1269, 2015.
- T. Krzeszowski, K. Przednowek, K. Wiktorowicz, and J. Iskra, “Estimation of hurdle clearance parameters using a monocular human motion tracking method,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 19, no. 12, pp. 1319–1329, 2016, PMID: 26838547.
- M. Clerc and J. Kennedy, “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002.
- R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, vol. 1, 2000, pp. 84–88.
- Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation, vol. 1, 2001, pp. 101–106.
- A. M. Abdelbar, S. Abdelshahid, and D. C. Wunsch, “Fuzzy PSO: a generalization of particle swarm optimization,” in Proceedings. IEEE International Joint Conference on Neural Networks, vol. 2, July 2005, pp. 1086–1091.
- H. Liu, A. Abraham, and W. Zhang, “A fuzzy adaptive turbulent particle swarm optimisation,” Int. J. Innov. Comput. Appl., vol. 1, no. 1, pp. 39–47, 2007.
- Y.-T. Juang, S.-L. Tung, and H.-C. Chiu, “Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” Information Sciences, vol. 181, no. 20, pp. 4539–4549, 2011, Special Issue on Interpretable Fuzzy Systems.
- J. J. D. Nesamalar, P. Venkatesh, and S. C. Raja, “Managing multiline power congestion by using Hybrid Nelder-Mead—Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO),” Applied Soft Computing, vol. 43, pp. 222–234, 2016.
- T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” Systems, Man and Cybernetics, IEEE Transactions on, no. 1, pp. 116–132, 1985.
- E. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1–13, 1975.
- G. Evers, “PSO Research Toolbox (Version 20110515), M.S. thesis code,” 2016. [Online]. Available: http://www.georgeevers.org/pso_research_toolbox.htm
- J. J. Liang, P. N. Suganthan, and K. Deb, “Novel composition test functions for numerical global optimization,” in Proceedings. IEEE Swarm Intelligence Symposium. SIS 2005, June 2005, pp. 68–75.
- K. Wiktorowicz, K. Przednowek, L. Lassota, and T. Krzeszowski, “Predictive modeling in race walking,” Computational Intelligence and Neuroscience, vol. 2015, p. 9, 2015, Article ID 735060.