Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search

  • Authors

    • Monica Sood
    • Dr. Sahil Verma
    • Vinod Kumar Panchal
    • Dr. Kavita
    2018-10-04
    https://doi.org/10.14419/ijet.v7i4.12.20987
  • Optimal Path Planning, Meta-heuristic algorithm, Cuckoo Search, Bat Algorithm, Swarm Intelligence, Robotics

  • Abstract

    Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

     

     

  • References

    1. [1]. Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.

      [2]. Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186.

      [3]. Mohanty, P. K., & Parhi, D. R. (2016). Optimal path planning for a mobile robot using cuckoo search algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(1-2), 35-52.

      [4]. Wang, G., Guo, L., Duan, H., Liu, L., & Wang, H. (2012). A bat algorithm with mutation for UCAV path planning. The Scientific World Journal, 2012.

      [5]. Mohanty, P. K., & Parhi, D. R. (2013, December). Cuckoo search algorithm for the mobile robot navigation. In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 527-536). Springer, Cham.

      [6]. Mohanty, P. K., & Parhi, D. R. (2016). Optimal path planning for a mobile robot using cuckoo search algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(1-2), 35-52.

      [7]. Das, P. K., Behera, H. S., Das, S., Tripathy, H. K., Panigrahi, B. K., & Pradhan, S. K. (2016). A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment. Neurocomputing, 207, 735-753.

      [8]. Ayari, A., & Bouamama, S. (2017). A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization. Robotics and Biomimetics, 4(1), 8.

      [9]. Englot, B., & Hover, F. (2011, May). Multi-goal feasible path planning using ant colony optimization. In Robotics and Automation (ICRA), 2011 IEEE International Conference on (pp. 2255-2260). IEEE.

      [10]. Wang, G., Guo, L., Duan, H., Liu, L., & Wang, H. (2012). A modified firefly algorithm for UCAV path planning. International Journal of Hybrid Information Technology, 5(3), 123-144.

      [11]. Das, P. K., Behera, H. S., & Panigrahi, B. K. (2016). A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation, 28, 14-28.

      [12]. Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.

      [13]. Yang, X. S., & Hossein Gandomi, A. (2012). Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5), 464-483.

      [14]. Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.

      [15]. Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.

  • Downloads

  • How to Cite

    Sood, M., Sahil Verma, D., Kumar Panchal, V., & Kavita, D. (2018). Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search. International Journal of Engineering & Technology, 7(4.12), 30-33. https://doi.org/10.14419/ijet.v7i4.12.20987

    Received date: 2018-10-04

    Accepted date: 2018-10-04

    Published date: 2018-10-04