ICSI 2015: Advances in Swarm and Computational Intelligence pp 460-467 | Cite as
An Improved Hybrid PSO Based on ARPSO and the Quasi-Newton Method
Abstract
Although attractive and repulsive particle swarm optimization (ARPSO) algorithm keeps the diversity of the swarm adaptively to avoid premature convergence, its search performance is still restricted because of its stochastic search mechanism. In this study, a new hybrid algorithm combining ARPSO with the Quasi-Newton method is proposed to improve the search ability of the swarm. In the proposed algorithm, ARPSO keeps the reasonable search space by controlling the swarm not to lose its diversity, while the Quasi-Newton method is used to perform local search efficiently. The Quasi-Newton method makes the hybrid algorithm converge to optimal solution accurately. The experimental results verify that the proposed hybrid PSO has better convergence performance than some classic PSO algorithms.
Keywords
Attractive and repulsive particle swarm optimization Diversity The Quasi-Newton methodPreview
Unable to display preview. Download preview PDF.
References
- 1.Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE International Conference on Neural Networks 4, 1942–1948 (1995)Google Scholar
- 2.Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: The Sixth International Symposium on Micro Machines and Human Science, pp. 39–43 (1995)Google Scholar
- 3.He, S., Wu, Q.H., Wen, J.Y.: A particle swarm optimizer with passive congregation. Biosystems 78, 135–147 (2004)CrossRefGoogle Scholar
- 4.Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: 1999 Congress on Evolutionary Computation, pp. 1951–1957 (1999)Google Scholar
- 5.Corne, D., Dorigo, M., Glover, F.: New ideas in optimization. McGraw Hill (1999)Google Scholar
- 6.Riget, J., Vesterstrom, J.S.: A diversity-guided particle swarm optimizer - the arPSO. Technical report. University of Aarhus, Department of Computer Science, Aarhus, Denmark (2002)Google Scholar
- 7.Han, F., Zhu, J.S.: Improved particle swarm optimization combined with backpropagation for feedforward neural networks. International Journal of Intelligent Systems 28(3), 271–288 (2013)CrossRefGoogle Scholar
- 8.Noel, M.M.: A new gradient based particle swarm optimization algorithm for accurate computation of global minimum. Applied Soft Computing 12(1), 353–359 (2012)CrossRefMathSciNetGoogle Scholar
- 9.Liu, Q., Han, F.: A hybrid attractive and repulsive particle swarm optimization based on gradient search. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (eds.) ICIC 2013. LNCS, vol. 7996, pp. 155–162. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 10.Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)Google Scholar
- 11.Battiti, R., Masulli, F.: BFGS optimization for faster and automated supervised learning. In: Proceedings of International Neural Network Conference, pp. 757–760 (1990)Google Scholar