Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm

  • Authors

    • M Goudhaman Jeppiaar Engineering College
    2018-08-22
    https://doi.org/10.14419/ijet.v7i3.18.14616
  • Swarm Intelligence, Metaheuristic Algorithm, Cheetah Chase Algorithm, Bat Algorithm and Lion Optimization Algorithm.

  • Abstract

    In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence. Many metaheuristic algorithms have been developed by inspiring various nature phenomena’s. Exploration and exploitation are distinctive capacities and confine each other, along these lines, customary calculations require numerous parameters and bunches of expenses to accomplish the adjust, and furthermore need to modify parameters for various enhancement issues. In this paper, another populace based algorithm, the Cheetah Chase Algorithm (CCA), is presented. Distinctive features of Cheetah and their characteristics has been the essential motivation for advancement of this optimization algorithm. Cheetah Chase Algorithm (CCA) has awesome capacities both in exploitation and exploration, is proposed to address these issues. To start with, CCA endeavours to locate the optimal solution in the assigned hunt territory. It at that point utilizes history data to pursue its prey. CCA can, hence, decide the situation of the worldwide ideal. CCA accomplishes solid exploitation and exploration with these highlights. Additionally, as indicated by various issues, CCA executes versatile parameter change. The self-examination and analysis of this exploration show that each CCA capacity can have different beneficial outcomes, while the execution correlation exhibits CCAs predominance over conventional metaheuristic algorithms. The proposed Cheetah Chase Algorithm is developed by the process of hunting and chasing of Cheetah to capture its prey with the parameters of high speed, velocity and greater accelerations.

     

     

  • References

    1. [1] M. Belal, J. Gaber, H. El-Sayed, and A. Almojel, Swarm Intelligence, In Handbook of Bioinspired Algorithms and Applications. Series: CRC Computer & Information Science. Vol. 7.Chapman & Hall Eds, 2006.ISBN 1-58488-444-5.

      [2] Yao-Hsin Chou, Shu-Yu Kuo, Li-Sheng Yang, and Chia-Yun Yang. Next Generation Metaheuristic: Jaguar Algorithm. https://doi.org/10.1109/ACCESS.2018.2797059.

      [3] Rory P Wilson, Iwan W Griffiths, Michael GL Mills, Chris Carbone, John W Wilson, David M Scantlebury, Mass enhances speed but diminishes turn capacity in terrestrial pursuit predators, eLife 2015;4:e06487 https://doi.org/10.7554/eLife.06487.

      [4] Elliot JP, McTaggart C, Holling CS. Prey capture by the African lion. Canadian Journal of Zoology. 1977; 55:1811–1828. https://doi.org/10.1139/z77-235.

      [5] Williams TM, Wolfe L, Davis T, Kendall T, Richter B, Wang Y, Bryce C, Elkaim GH, Wilmers CC. Instantaneous energetics of puma kills reveal advantage of felid sneak attacks. Science. 2014; 346:81–85. https://doi.org/10.1126/science.1254885.

      [6] Yao-Hsin Chou, Shu-Yu Kuo, Li-Sheng Yang, and Chia-Yun Yang. Next Generation Metaheuristic: Jaguar Algorithm. https://doi.org/10.1109/ACCESS.2018.2797059.

      [7] BLUM, C., ROLI, A., 2003. Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison, ACM Computing Surveys, Vol. 35, No. 3, September 2003, pp. 268–308.

      [8] Goudhaman.M, Vanathi.N, Sasikumar.S, Frequency Synchronization Enhancement in Wireless Sensor Network using BAT Algorithm in International Journal of Engineering and Technology (IJET) Vol 9 No 6 Dec 2017-Jan 2018, DOI: 10.21817/ijet/2017/v9i6/170906151, ISSN (Print) : 2319-8613 ISSN (Online) : 0975-4024.

      [9] MaziarYazdani, FariborzJolai, Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm, Journal of Computational Design and Engineering 3(2016)24–36, www.sciencedirect.com, https://doi.org/10.1016/j.jcde.2015.06.003.

      [10] IztokFister Jr, Xin-She Yang, IztokFister, Janez Brest, DusanFister. A Brief Review of Nature-Inspired Algorithms for Optimization, Elektrotehniskivestnik 80(3): 1–7, 2013

      [11] Xin-She Yang and Amir H. Gandomi, Bat Algorithm: A Novel Approach for Global Engineering Optimization, Engineering Computations, Vol. 29, Issue 5, pp. 464--483 (2012). https://doi.org/10.1108/02644401211235834.

      [12] Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, UK (2010).

      [13] Yang X-S (2010) A new metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Eds. Cruz C., Gonzalez J., Krasnogor N., and Terraza G.), Springer, SCI 284, pp 65-74.https://doi.org/10.1007/978-3-642-12538-6_6.

      [14] J.,Liang, B. Qu, and P.Suganthan, Problem definitions and evaluation criteria for the CEC2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelligence Laboratory,2013.

      [15] Oftadeh R, Mahjoob M, Shariatpanahi M. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math.Appl. 2010 60(7)2087–98.https://doi.org/10.1016/j.camwa.2010.07.049.

      [16] Xin Chen, Yongquan Zhou, and Qifang Luo, A Hybrid Monkey Search Algorithm for Clustering Analysis, The Scientific World Journal Volume 2014 (2014), Article ID 938239, 16 pages, https://doi.org/10.1155/2014/938239.

      [17] https://cheetah.org/about-the-cheetah/.

      [18] http://c21.phas.ubc.ca/article/cheetah-chase.

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  • How to Cite

    Goudhaman, M. (2018). Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm. International Journal of Engineering & Technology, 7(3), 1804-1811. https://doi.org/10.14419/ijet.v7i3.18.14616

    Received date: 2018-06-23

    Accepted date: 2018-07-17

    Published date: 2018-08-22