Abstract
This paper talks about the problems in particle swarm optimization (PSO), including local optimum and difficulty in improving solution accuracy by fine tuning. We presents a new variation of Adaptive Tribe-PSO model where nonlinear updating of inertia weight and a particle’s fitness with Tribe-PSO model are combined to improve the speed of convergence as well as fine tune the search in the multidimensional space. The method proved to be a powerful global optimization algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Liang, J.J., Qin, A.K., Suganthan, P.N., Bizataskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295
Zhan, Z.-H., Zhang, J., Li, Y., Chung, H.S.-H.: Adaptive Particle Swarm Optimization. IEEE Trans. Syst., Man, Cybern. C 39(6), 1362–1381 (2009)
Chen, K., Li, T.H., Cao, T.C.: Tribe-PSO: A novel global optimization algorithm and its application in molecular docking. Chemometrics and Intelligent Laboratory Systems 82(1-2), 248–259 (2006)
Chatterjee, A., Siarry, P.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput. Oper. Res. 33(3), 859–871 (2004)
Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: Proc. IEEE Congr. Evol. Comput., vol. 1, pp. 101–106 (2001)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. IEEE World Congr. Comput. Intell., pp. 69–73 (1998)
Ratnaweera, A., Halgamuge, S., Watson, H.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)
Tripathi, P.K., Bandyopadhyay, S., Pal, S.K.: Adaptive multi-objective particle swarm optimization algorithm. In: Proc. IEEE Congr. Evol. Comput., Singapore, pp. 2281–2288 (2007)
Ratnaweera, A., Halgamuge, S., Watson, H.: Particle swarm optimization with self-adaptive acceleration coefficients. In: Proc. 1st Int. Conf. Fuzzy Syst. Knowl. Discovery, pp. 264–268 (2003)
Yamaguchi, T., Yasuda, K.: Adaptive particle swarm optimization: Self-coordinating mechanism with updating information. In: Proc. IEEE Int. Conf. Syst., Man, Cybern., Taipei, Taiwan, pp. 2303–2308 (October 2006)
Gong, C., Wang, Z.L.: Optimization calculation based on MATLAB, pp. 283–285. Publish House of Electronics Industry, Beijing (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, Y.D., Zhang, L., Han, P. (2011). An Adaptive Tribe-Particle Swarm Optimization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-21515-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21514-8
Online ISBN: 978-3-642-21515-5
eBook Packages: Computer ScienceComputer Science (R0)