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3 BOYUTLU ORTAMDA YOL PLANLAMASI İÇİN KULLANILABİLECEK GELİŞMİŞ PARÇACIK SÜRÜ ENİYİLEME YÖNTEMİ

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

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1
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14

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Abstract (Original Language): 
Parçacık sürü eniyileme yöntemi nispeten yeni bir nüfus temelli yapay zekâ algoritması olup, eniyileme problemlerinde oldukça iyi performans sergileyebilmektedir. Bununla berber eniyileme süreci esnasında sürü içersindeki parçacıklar gittikçe birbirlerine benzemekte ve çoğunlukla da sürü içersindeki en iyi parçacık etrafında öbeklenmektedirler. Bu öbeklenme ise eniyileme sürecini genel çözüm yerine yerel çözümle sonlandırabilmektedir. Bununla beraber yöntem ilave mutasyon operatörü kullanılarak geliştirilebilir ve bu sayede sürü içersindeki çeşitlilik arttırılarak yerel çözüm yerine genel çözüme ulaşılabilir. Bu makalede öncelikle yöntemin matematiksel temelleri gözden geçirilmiş, çeşitlilik kavramı üzerinde durularak çeşitliliğin bazı parçacık sürü algoritmalarındaki davranışları incelenmiştir. Ayrıca bu çözümlemeye dayalı olarak geliştirilen periyodik mutasyon uygulamaları yeni parçacık sürü yönteminde uygulamaya konulmuştur. Geliştirilen yeni yöntemin verimliliğini göstermek için değişik test fonksiyonları ile yol planlama problemleri çözülerek farklı algoritmalarla karşılaştırmalara gidilmiştir. Elde edilen sonuçlar periyodik mutasyon uygulamalarının etkilerini ve bu sayede elde edilen yüksek verimliliği teyit eder niteliktedir.
Abstract (2. Language): 
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.

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REFERENCES

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 swarmexplosion,
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)

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