A NEW PARTICLE SWARM OPTIMIZATION METHOD FOR THE PATH PLANNING OF UAV IN 3D ENVIRONMENT

  • Yasin Volkan Pehlivanoğlu
Keywords: PSO, Diversity, Periodicity, Path planning

Abstract

Particle swarm optimization (PSO) method is relatively a new population-based intelligence algorithm and exhibits good performance in optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged possibly around the local solution. PSO technique can be augmented with an additional mutation operator that provides diversity and helps prevent premature convergence on local optima. In this paper, mathematical analysis of a basic PSO is reissued and a diversity concept is evaluated in commonly used PSO algorithms including constriction factor PSO, inertial weight PSO, Gaussian mutation PSO, and a new vibrational mutation PSO combining the idea of mutation strategy related to periodicity. New algorithm is tested and compared with selected PSO algorithms. The comparative experiments have been conducted on a wide range of nonlinear functions and a path planning problem of unmanned aerial vehicle (UAV) in three-dimensional (3D) terrain environment. The results give insight into how mutation operator effects the nature of the diversity and show that the addition of a mutation operator with a periodicity concept can significantly enhance the optimization performance.   

References

[1] Eberhart, R. C., Kennedy, J.: A new optimizer using particle swarm theory. Proc. 6th Int. Symp. Micromachine Human Sci., Nagoya, Japan, pp. 39–43, (1995)
[2] Valle, Y., Venayagamoorthy, Mohagheghi, G. K., S., Hernandez, J.-C., Harley, R. G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput., vol. 12, no. 2, pp. 171-195, (2008)
[3] Shi, Y., Eberhart, R.: Parameter selection in particle swarm optimization. Proc. 7th Annual Conf. on Evol. Prog., pp. 591-601, (1998)
[4] Higashi, H., Iba, H.: Particle swarm optimization with Gaussian mutation. Proc. of the IEEE Swarm Intell. Symp., pp. 72 – 79, (2003)
[5] Stacey, A., Jancic, M., Grundy, I.: Particle swarm optimization with mutation. Proc. of the IEEE Congr. Evol. Comput., pp. 1425-1430, (2003)
[6] Pant, M., Thangaraj, R., Singh, V. P., Abraham, A.: Particle swarm optimization using Sobol mutation. IEEE 1st Int. Conf. on Emerging Trends in Eng. and Tech., pp. 367-372, (2008)
[7] Andrews, P. S.: An investigation into mutation operators for particle swarm optimization. IEEE Congr. Evol. Comput., pp. 1044-1051, (2006)
[8] Zavala, A. E., Aguirre, A. H., Diharce, E. R. V., Rionda, S. B.: Constrained optimization with an improved particle swarm optimization algorithm. Int. J. Intell. Comput. and Cybern., vol. 1, no. 3, pp. 425-453, (2008)
[9] Jia, D., Li, L., Zhang, Y., Chen, X.: Particle swarm optimization combined with chaotic and Gaussian mutation. Proc. of the 6th World Congr. Intell. Cont. Auto., pp. 3281-3285, (2006)
[10] Wu, X., Cheng, B., Cao, J., Cao, B.: Particle swarm optimization with normal cloud mutation. Proc. on the 7th World Congr. Intell. Cont. Auto., pp.2828-2832, (2008)
[11] Chen, J., Ren, Z., Fan, X.: Particle swarm optimization with adaptive mutation and its application research in tuning of PID parameters. 1st Int.. Symp. Syst. Cont. Aerospace and Astronautics, pp.990-994, (2006)
[12] Yang, M., Huang, H., Xiao, G.: A novel dynamic particle swarm optimization algorithm based on chaotic mutation. IEEE 2nd Int. Workshop on Knowledge Discovery and Data Mining, pp.656-659, (2009)
[13] Liu, J., Fan, X., Qu, Z.: An improved particle swarm optimization with mutation based on similarity. 3rd Int.. Conf. Natural Comput., pp. 824-828, (2007)
[14] Biao, C., Zhishu, L., Die, F., Jian, H., Peng, O., Qing, L.: Mutated fast convergent particle swarm optimization and convergence analysis. IEEE 1st Int. Conf. on Intell. Netw. Intell. Syst., pp. 5-8, (2008)
[15] Ozcan, E., Mohan, C. K.: Particle swarm optimization: Surfing the waves. Proc. 1999 Congr. Evol. Comput., Washington, DC, pp. 1939–1944, (1999)
[16] Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58–73, (2002)
[17] Shi, Y., Eberhart, R.: A modified particle swarm optimizer. Proc. of the World Congr. Comput. Intell., pp. 69–73, (1998)
[18] Pant, M., Radha, T., Singh, V. P.: A New Diversity Based Particle Swarm Optimization using Gaussian Mutation. Int. J. Math. Modeling, Simulation and Appl., 1(1), pp. 47-60, (2008)
[19] Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J-M.: Wavelets and Their Applications, ISTE Ltd., USA, pp. 8-12, (2007)
[20] Farin, G.: Curves and Surfaces for Computer Aided Geometric Design; A Practical Guide, Academic Press, Inc., pp. 41-42, (1993)
Published
2012-07-23
How to Cite
[1]
Y. Pehlivanoğlu, “A NEW PARTICLE SWARM OPTIMIZATION METHOD FOR THE PATH PLANNING OF UAV IN 3D ENVIRONMENT”, JAST, vol. 5, no. 4, pp. 1-14, Jul. 2012.
Section
Articles