Open Access
ITM Web Conf.
Volume 48, 2022
The 4th International Conference on Computing and Wireless Communication Systems (ICCWCS 2022)
Article Number 03007
Number of page(s) 8
Section Computer Science, Intelligent Systems and Information Technologies
Published online 02 September 2022
  1. E. Masehian and D. Sedighizadeh, “Classic and Heuristic Approaches in Robot Motion Planning – A Chronological Review,” World Acad. Sci. Eng. Technol., vol. 1, no. 5, 2007. [Google Scholar]
  2. M. Colnarič, L. P. Behnck, D. Doering, C. E. Pereira, and A. Rettberg, “A Modified Simulated Annealing Algorithm for SUAVs Path Planning,” in 2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics, Maribor, Slovenia, Jun. 2015, vol. 48, pp. 63–68. doi: 10.1016/j.ifacol.2015.08.109. [Google Scholar]
  3. X. Zhang and H. Duan, “An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning,” Appl. Soft Comput., vol. 26, pp. 270–284, Jan. 2015, doi: 10.1016/j.asoc.2014.09.046. [CrossRef] [Google Scholar]
  4. Y. V. Pehlivanoglu, “A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV,” Aerosp. Sci. Technol., vol. 16, no. 1, pp. 47–55, Jan. 2012, doi: 10.1016/j.ast.2011.02.006. [CrossRef] [Google Scholar]
  5. Y. Fu, M. Ding, and C. Zhou, “Phase Angle- Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV,” IEEE Trans. Syst. Man Cybern. - Part Syst. Hum., vol. 42, no. 2, pp. 511–526, Mar. 2012, doi: 10.1109/TSMCA.2011.2159586. [CrossRef] [Google Scholar]
  6. U. Cekmez, M. Ozsiginan, and O. K. Sahingoz, “A UAV path planning with parallel ACO algorithm on CUDA platform,” in 2014 International Conference on Unmanned Aircraft Systems (ICUAS), May 2014, pp. 347–354. doi: 10.1109/ICUAS.2014.6842273. [CrossRef] [Google Scholar]
  7. Y. Chen, J. Yu, Y. Mei, Y. Wang, and X. Su, “Modified central force optimization (MCFO) algorithm for 3D UAV path planning,” Neurocomputing, vol. 171, pp. 878–888, Jan. 2016, doi: 10.1016/j.neucom.2015.07.044. [CrossRef] [Google Scholar]
  8. W. Zhu and H. Duan, “Chaotic predator–prey biogeography-based optimization approach for UCAV path planning,” Aerosp. Sci. Technol., vol. 32, no. 1, pp. 153–161, Jan. 2014, doi: 10.1016/j.ast.2013.11.003. [CrossRef] [Google Scholar]
  9. C. Xu, H. Duan, and F. Liu, “Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning,” Aerosp. Sci. Technol., vol. 14, no. 8, pp. 535–541, Dec. 2010, doi: 10.1016/j.ast.2010.04.008. [CrossRef] [Google Scholar]
  10. S. Zhang, Y. Zhou, Z. Li, and W. Pan, “Grey wolf optimizer for unmanned combat aerial vehicle path planning,” Adv. Eng. Softw., vol. 99, pp. 121–136, Sep. 2016, doi: 10.1016/j.advengsoft.2016.05.015. [CrossRef] [Google Scholar]
  11. V. Roberge, M. Tarbouchi, and G. Labonte, “Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning,” Ind. Inform. IEEE Trans. On, vol. 9, no. 1, pp. 132–141, Feb. 2013, doi: 10.1109/TII.2012.2198665. [CrossRef] [Google Scholar]
  12. V. Roberge, M. Tarbouchi, and F. Allaire, “Parallel Hybrid Metaheuristic on Shared Memory System for Real-Time UAV Path Planning,” Int. J. Comput. Intell. Appl., vol. 13, no. 2, pp. 1450008-1-1450008–16, Jun. 2014. [CrossRef] [Google Scholar]
  13. Z. A. A. Alyasseri, A. T. Khader, M. A. Al-Betar, M. A. Awadallah, and X.-S. Yang, “Variants of the Flower Pollination Algorithm: A Review,” in Nature-Inspired Algorithms and Applied Optimization, X.-S. Yang, Ed. Cham: Springer International Publishing, 2018, pp. 91–118. doi: 10.1007/978-3-319-67669-2_5. [CrossRef] [Google Scholar]
  14. S. Pant, A. Kumar, and M. Ram, “Flower pollination algorithm development: a state of art review,” Int. J. Syst. Assur. Eng. Manag., vol. 8, no. 2, pp. 1858–1866, Nov. 2017, doi: 10.1007/s13198-017-0623-7. [CrossRef] [Google Scholar]
  15. H. Chiroma, N. L. M. Shuib, S. A. Muaz, A. I. Abubakar, L. B. Ila, and J. Z. Maitama, “A Review of the Applications of Bio-inspired Flower Pollination Algorithm,” Procedia Comput. Sci., vol. 62, pp. 435–441, Jan. 2015, doi: 10.1016/j.procs.2015.08.438. [CrossRef] [Google Scholar]
  16. Government of Canada, “Canadian Digital Elevation Model.” (accessed May 18, 2022). [Google Scholar]
  17. G. Labonté, “Sur la construction de trajectories dynamiquement réalisables pour les avions à partir de suites de segments de droites,” Collège militaire royal du Canada, Kingston, Ontario, Canada, Nov. 2009. [Google Scholar]
  18. G. Labonté, “Simple formulas for the fuel of climbing propeller driven airplanes,” Adv. Aircr. Spacecr. Sci., vol. 2, no. 4, pp. 367–389, 2015, doi: [CrossRef] [Google Scholar]
  19. Y. Ding et al., “Discussions on Normalization and Other Topics in Multi-Objective Optimization,” Toronto, Aug. 2006. [Online]. Available: [Google Scholar]
  20. J. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975. [Google Scholar]
  21. H. Li, S.-Y. Liu, Y.-W. Huang, Y.-Q. Chen, and Z.-H. Fu, “An Efficient 2-opt Operator for the Robotic Task Sequencing Problem,” in 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR), Aug. 2019, pp. 124–129. doi: 10.1109/RCAR47638.2019.9044008. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.