Jeng-Shyang Pan
ABSTRACT
This paper applies an improved Cuckoo Search algorithm, named Chaotic CS algorithm, to solve Unmanned Combat Aerial Vehicle (UCAV) path planning problems. A circle-type chaotic map for generating chaotic sequences is used to specify the scaling factor (() of step size and fraction probability (pa) of abandonment for host nests formulated in the Original CS algorithm. The advantage of using Chaotic CS algorithm can dynamically change the parameters of ( and pa by using the chaotic sequences over the course of iterations, resulting in an improvement for searching performance to find out the global best solution. The enhanced CS algorithm shows flexible and robust capabilities to optimize complex and multimodal objective functions by evaluating standard benchmark functions. Furthermore, the Chaotic CS algorithm is applied to solve complex design problem. Two scenarios of UCAV path planning problems are carried out for the practical applications. The simulation results indicate that the Chaotic CS algorithm can efficiently be used for computing optimal flight path of UCAV.
- Yang, X. S. and Deb, S. 2009. Cuckoo search via Lévy flights. In Proceedings of World Congress on Nature & Biologically Inspired Computing (Coimbatore, India, December 09--11, 2009), IEEE Press, Piscataway, NJ. 210--214.Google Scholar
- Yang, X. S. and Deb, S. 2010. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1, 4 (Dec. 2010) 330--334.Google ScholarCross Ref
- Civicioglu, P. and Besdok E. 2013. A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39, 4(April 2013), 315--346. Google ScholarDigital Library
- Clerc M. and Kennedy J. 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evolutionary Computation, 6, 1 (Aug. 2002), 58--73. Google ScholarDigital Library
- Price K., Storn R., and Lampinen, J. 2005. Differential evolution: a practical approach to global optimization. Natural Computing Series, Springer, Berlin, Germany. Google ScholarDigital Library
- Karaboga, D. and Basturk, B. 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 3 (April 2007), 459--471. Google ScholarDigital Library
- Walton S., Hassan, O., Morgan, K., and Brown, M. R. 2011. Modified cuckoo search: A new gradient free optimisation algorithm, Chaos, Solitons & Fractals, 44, 9 (Sept. 2011), 710--718.Google ScholarCross Ref
- Shi Y. and Eberhart, R. C. 1998. A modified particle swarm optimizer. In IEEE International Conference on Evolutionary (Anchorage, AK, May 04-09, 1998), 69--73.Google Scholar
- Valian, E., Mohanna, S., and Tavakoli, S. 2011. Improved cuckoo search algorithm for global optimization. International Journal of Communications and Information Technology, 1, 1 (Jan. 2011), 31--44.Google Scholar
- Kuang, F., Jin, Z., Xu, W., and Zhang S. 2014. A novel chaotic artificial bee colony algorithm based on tent map. In 2014 IEEE Congress on Evolutionary Computation (Beijing, China, July 06-11, 2014), 235--241.Google ScholarCross Ref
- Wang L. and Zhong, Y. 2015. Cuckoo search algorithm with chaotic maps. Mathematical Problems in Engineering, Hindawi Publishing Corporation, Cairo, Egypt, 2015 (July 2015), 1--14.Google Scholar
- Kaur, G. and Arora, S. 2018. Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5, 3 (July 2018), 275--284.Google ScholarCross Ref
- Nasa-Ngium, P., Sunat, K., and Chiewchanwattana, S. 2013. Enhancing modified cuckoo search by using Mantegna Lévy flights and chaotic sequences. In Proceedings of the 10th International Joint Conference on Computer Science and Software Engineering (Maha Sarakham, Thailand, May 29-31, 2013), 53--57.Google Scholar
- Ouyang, A., Pan, G., Yue G., and Du, J. 2014. Chaotic cuckoo search algorithm for high-dimensional functions. Journal of Computers, 9, 5 (May 2014), 1282--1290.Google ScholarCross Ref
- Dong, Y., Zhang Z., and Hong, W. H. 2018. A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting. Energies, 2018, 11, 4:1009 (April 2018), 1--21.Google Scholar
- Boushaki, S. I., Kamel, N., and Bendjeghaba, O. 2018. A new quantum chaotic cuckoo search algorithm for data clustering. Expert Systems with Applications, 96 (Dec. 2017), 358--372. Google ScholarDigital Library
- Chen, X., Chen, X. M., and Zhang, J. 2014. The dynamic path planning of UAV based on A* algorithm. Applied Mechanics and Materials, 494--495 (Feb. 2014), 1094--1097.Google Scholar
- Cao, L. and Zhang, A. 2013. Application of pre-evolution genetic algorithm in fast path planning for UCAV. Advances in Computer Science: an International Journal, 2, 6 (Nov. 2013), 74--79.Google Scholar
- Fu, Z. F. 2012. Path planning of UCAV based on a modified GeesePSO algorithm. Intelligent Computing Theories and Applications, Lecture Notes in Computer Science, 7390, Springer, Berlin, (July 2012), 471--478. Google ScholarDigital Library
- Chen, M., Wu Q. X., and Jiang C. S. 2008. A modified ant optimization algorithm for path planning of UCAV. Applied Soft Computing, 8, 4 (Sept. 2008), 1712--1718. Google ScholarDigital Library
- Wang, G., Guo L., Duan, H., Liu, L., Wang, H., and Wang J. 2012. A hybrid meta-heuristic DE/CS algorithm for UCAV path planning. Journal of Information & Computational Science, 9, 16 (Dec. 2012), 4811--4818.Google Scholar
- Li, B., Gong, L., and Zhao, C. 2013. Unmanned combat aerial vehicles path planning using a novel probability density model based on artificial bee colony algorithm. In 2013 Fourth International Conference on Intelligent Control and Information (Beijing, China, June 09-11, 2013), 620--625.Google Scholar
- Zhang, Y., Wu, L., and Wang, S. 2013. UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization. Mathematical Problems in Engineering, 2013 (July 2013), 1--9.Google Scholar
- Li, B., Gong, L. G., and Yang, W. L. 2014. An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning. The Scientific World Journal, 2014 (Mar. 2014), 1--10.Google Scholar
Index Terms
- Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems
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