A.Albert Alexe This email address is being protected from spambots. You need JavaScript enabled to view it.1, F. Sagayaraj francis2, and R. Subramanian3

1Research Scholar, Department of Computer Science, Pondicherry University, India
2Professor, Department of Computer Science and Engineering, Puducherry Technological University
3Professor, Department of Computer Science, Pondicherry University


Received: January 20, 2021
Accepted: February 19, 2021
Publication Date: June 24, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202108_24(6).0015  


The naturally inspired algorithm plays an important role in solving optimization problem. Naturally, inspired algorithm is used to search the optimized result from the large dataset. Many algorithms have been proposed already to solve the complex problem and result was proved. In this paper, we propose a new algorithm called Great Dane natural inspired search algorithm. This algorithm inspires the behaviour of pet animal Great Dane dog. The behaviour of Great Dane is very strong to protect the home. The behaviour of Great Dane is used to find the optimal solution from the large data base. The proposed algorithm is used to solve the single and multi-variable cost function problems through the generation of binary and integer structured Great Dane. The Great Dane algorithm is tested and implemented using De-Jong Type I function and the results provide the performance of the algorithm is better than the other evolutionary algorithms.

Keywords: Great Dane algorithm, Home defence, Home takeover, mating


  1. [1] John R Koza and John R Koza. Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press, 1992.
  2. [2] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1):29–41, 1996.
  3. [3] BR Rajakumar. The Lion’s Algorithm: a new natureinspired search algorithm. 2nd International conference on communication, Computing & security (Elsevier), Procedia, pages 126–135, 2012.
  4. [4] Xin She Yang, Mehmet Karamanoglu, and Xingshi He. Multi-objective flower algorithm for optimization. Procedia Computer Science, 18:861–868, 2013.
  5. [5] Sureshkumar Sudabattula and M. Kowsalya. Distributed Energy Resources Allocation Using Flower Pollination Algorithm in Radial Distribution Systems. Energy Procedia, 103:76–81, 2016.
  6. [6] Haruna Chiroma, Abdullah Khan, Adamu I Abubakar, Younes Saadi, Mukhtar F. Hamza, Liyana Shuib, Abdulsalam Y. Gital, and Tutut Herawan. A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Applied Soft Computing Journal, 48:50–58, 2016.
  7. [7] Xin She Yang. Flower pollination algorithm for global optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7445 LNCS:240–249, 2012.
  8. [8] Yu Peng Chen, Ying Li, GangWang, Yue Feng Zheng, Qian Xu, Jia Hao Fan, and Xue Ting Cui. A novel bacterial foraging optimization algorithm for feature selection. Expert Systems with Applications, 83:1–17, oct 2017.
  9. [9] Guang Yu Zhu andWei Bo Zhang. Optimal foraging algorithm for global optimization. Applied Soft Computing Journal, 51:294–313, feb 2017.
  10. [10] Sakkayaphop Pravesjit. A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. Artificial Life and Robotics, 21(1):112–119, mar 2016.
  11. [11] Ilyes Khennak and Habiba Drias. Bat-Inspired Algorithm Based Query Expansion for MedicalWeb Information Retrieval. Journal of Medical Systems, 41(2), feb 2017.
  12. [12] Najmeh Sadat Jaddi, Salwani Abdullah, and Abdul Razak Hamdan. Optimization of neural network model using modified bat-inspired algorithm. Applied Soft Computing Journal, 37:71–86, dec 2015.
  13. [13] Vassilios I Skoullis, Ioannis X Tassopoulos, and Grigorios N Beligiannis. Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm. Applied Soft Computing Journal, 52:277–289, mar 2017.
  14. [14] S. Chu, P. Tsai, and J. Pan. LNAI 4099 - Cat Swarm Optimization. 2006.
  15. [15] Tian qi Wu, Min Yao, and Jian hua Yang. Dolphin swarm algorithm. Frontiers of Information Technology and Electronic Engineering, 17(8):717–729, aug 2016.
  16. [16] A. Kaveh and N Farhoudi. A new optimization method: Dolphin echolocation. Advances in Engineering Software, 59:53–70, 2013.
  17. [17] Saeed Gholizadeh and Hamed Poorhoseini. Seismic layout optimization of steel braced frames by an improved dolphin echolocation algorithm. Structural and Multidisciplinary Optimization, 54(4):1011–1029, oct 2016.
  18. [18] Lifang He and Songwei Huang. Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing, 240:152–174, may 2017.
  19. [19] Hui Wang, Wenjun Wang, Xinyu Zhou, Hui Sun, Jia Zhao, Xiang Yu, and Zhihua Cui. Firefly algorithm with neighborhood attraction. Information Sciences, 382- 383:374–387, mar 2017.
  20. [20] Homa Amirian and Rashed Sahraeian. Solving a grey project selection scheduling using a simulated shuffled frog leaping algorithm. Computers and Industrial Engineering, 107:141–149, 2017.
  21. [21] Kaushik Kumar Bhattacharjee and Sarada Prasad Sarmah. A modified shuffled frog leaping algorithm with genetic mutation for combinatorial optimization.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7654 LNAI(PART 2):513–522, 2012.
  22. [22] M. T. Vakil Baghmisheh, Katayoun Madani, and Alireza Navarbaf. A discrete shuffled frog optimization algorithm. Artificial Intelligence Review, 36(4):267–284, dec 2011.
  23. [23] Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis. Grey Wolf Optimizer. Advances in Engineering Software, 69:46–61, 2014.
  24. [24] Mehak Kohli and Sankalap Arora. Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5(4):458–472, oct 2018.
  25. [25] Moumita Pradhan, Provas Kumar Roy, and Tandra Pal. Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Engineering Journal, 9(4):2015–2025, dec 2018.
  26. [26] Manizheh Ghaemi and Mohammad Reza Feizi- Derakhshi. Forest optimization algorithm. Expert Systems with Applications, 41(15):6676–6687, nov 2014.
  27. [27] Manizheh Ghaemi and Mohammad Reza Feizi- Derakhshi. Feature selection using Forest Optimization Algorithm. Pattern Recognition, 60:121–129, dec 2016.
  28. [28] Najmeh Sadat Jaddi, Jafar Alvankarian, and Salwani Abdullah. Kidney-inspired algorithm for optimization problems. Communications in Nonlinear Science and Numerical Simulation, 42:358–369, jan 2017.
  29. [29] Peifeng Niu, Ke Chen, Yunpeng Ma, Xia Li, Aling Liu, and Guoqiang Li. Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm. Knowledge-Based Systems, 118:80– 92, feb 2017.
  30. [30] Asaju La aro Bolaji, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Ahamad Tajudin Khader, and Laith Mohammad Abualigah. A comprehensive review: Krill Herd algorithm (KH) and its applications. Applied Soft Computing Journal, 49:437–446, dec 2016.
  31. [31] Gaige Wang, Lihong Guo, Heqi Wang, Hong Duan, Luo Liu, and Jiang Li. Erratum to Incorporating mutation scheme into krill herd algorithm for global numerical optimization (Neural Comput & Applic, 10.1007/s00521-012-1304-8). Neural Computing and Applications, 24(5):1231, apr 2014.
  32. [32] M. K. Marichelvam, Ömür Tosun, and M. Geetha. Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time. Applied Soft Computing Journal, 55:82–92, jun 2017.
  33. [33] Kavita Gupta, Kusum Deep, and Jagdish Chand Bansal. Spider monkey optimization algorithm for constrained optimization problems. Soft Computing, 21(23):6933–6962, dec 2017.
  34. [34] Avinash Sharma, Akshay Sharma, B. K. Panigrahi, Deep Kiran, and Rajesh Kumar. Ageist Spider Monkey Optimization algorithm. Swarm and Evolutionary Computation, 28:58–77, jun 2016.
  35. [35] Yunzhi Jiang, Pohsiang Tsai, Wei Chang Yeh, and Longbing Cao. A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem. Applied Soft Computing Journal, 52:1181–1190, mar 2017.
  36. [36] Patcharawadee Poolsamran and Arit Thammano. A modified marriage in Honey-bee Optimization for function optimization problems. Procedia Computer Science, 6:335–342, 2011.
  37. [37] John Galletly. Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Kybernetes, 27(8):979–980, nov 1998.
  38. [38] GEATbx: Example Functions (single and multiobjective functions) 2 Parametric Optimization. GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with Matlab, 2006.


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