Abstract
The premature problem is always being a hot topic in the swarm intelligence research field. PSO could easily fall into local optima because the particles could quickly get closer to the best particle. To this end, this paper proposes a new hybrid PSO named HGC-PSO to solve this problem. The mutation mainly considers the m+1 particles which have the better fitness values. Firstly, we add the Gauss mutation to the current global optimal. Secondly, we use the Cauchy mutation to change the rest of the m+1 particles. The purpose of this method is to increase the population diversity and avoid the PSO fall into local optima. Finally, HGC-PSO is applied to path planning problem in 3D space for robot in this paper. The experiment of results prove that the proposed algorithm has higher convergence speed and precision, besides a path without collision is found.
This paper is partially sponsored by National Natural Science Foundation of China Grant (51179039), and by grant from the PH. D. Programs Foundation of Ministry of Education of China (20102304110021).
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References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int’l. Conf. on Neural Networks, IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Lovbjerg, M., Krink, T.: Extending Particle Swarm Optimisers with Self-Organized Criticality. In: Proceedings of Fourth Congress on Evolutionary Computation, vol. 2, pp. 1588–1593 (2002)
Coelho, L.S., Krohling, R.A.: Predictive controller tuning using modified particle swarm optimization based on Cauchy and Gaussian distributions. In: Proceedings of the 8th On-Line World Conference on Soft Computing in Industrial Applications, WSC8 (2003)
Feller, W.: An Introduction to Probability Theory and Its Applications, 2nd edn., vol. 2. John Wiley & Sons, Inc. (1971); van den Bergh, F.: An Analysis of Particle Swarm Optimizers(1971)
Caroll, K.P., McClaran, S.R., Nelson, E.L., et al.: AUV Path Planning: An A* Approach to Path Planning width Consideration of Variable Vehicle Speeds and Multiple, Overlapping, Time-Dependent Exclusion Zones. In: Proc. Sym. on AUV Technology, pp. 79–84. IEEE, Washington, DC (1992)
Lee, S., Park, J.: Neural computation for collisionfree path planning. Journal of Intelligent Manufacturing (2), 315–326 (1991)
Holland, J.H.: Adaptation in Natural and Artificial System. The University of Michigan Press, Ann Arbor (1975)
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Lv, C., Yu, F., Yang, N., Feng, J., Zou, M. (2012). The Application of a Hybrid Algorithm to the Submersible Path-Planning. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_57
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DOI: https://doi.org/10.1007/978-3-642-30976-2_57
Publisher Name: Springer, Berlin, Heidelberg
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