Crocodile Hunting Strategy (CHS): A comparative study using benchmark functions

Document Type : Research Article

Author

• A. R. Balavand

Department of Industrial Engineering, Science and Research Branch, Islamic Azad University.

10.22067/ijnao.2022.71418.1049

Abstract

The crocodiles have a good strategy for hunting the fishes in nature. These creatures are divided into two groups of chasers and ambushers when hunt-ing. The chasers direct prey toward shallow water with a powerful splash of its tail without catching them, and the ambushers wait in the shallow and try to snatch the fishes. Such behavior inspires the development of a new population-based optimization algorithm called the crocodile hunting strategy (CHS). In order to verify the performance of the CHS, several classical benchmark functions and four constrained engineering design op-timization problems are used. In the classical benchmark function, the comparisons are performed using ant colony optimization, differential evo-lution, genetic algorithm, and particle swarm optimization. Constrained engineering design problems are compared with firefly algorithm, harmony search, shuffled frog-leaping algorithm, and teaching-learning-based opti-mization. The results of the comparison show that different operators de-signed in the CHS algorithm lead to fast algorithm convergence and show better results compared to other algorithms.

Keywords

• Crocodile hunting strategy
• Optimization algorithms
• Numer-ical optimization
• Classical benchmark functions
• Constrained engineering design problem

20.1001.1.24236977.2022.12.2.8.4

Main Subjects

• Optimization

---

References

1.Abdel-Basset, M., Abdel-Fatah, L. and Sangaiah, A.K. Metaheuristic al-gorithms: A comprehensive review, Computational intelligence for multi-media big data on the cloud with engineering applications, 2018, Elsevier, 185–231.
2. Akay, B. and Karaboga, D. A modified artificial bee colony algorithm for real-parameter optimization, Inf. Sci. 192 (2012), 120–142.
3. Alba, E. and Dorronsoro, B. The exploration/exploitation tradeoff in dynamic cellular genetic algorithms, IEEE Trans. Evol. Comput. 9(2) (2005), 126–142.
4. Askarzadeh, A. Bird mating optimizer: an optimization algorithm in-spired by bird mating strategies, Commun. Nonlinear Sci. Numer. Simul. 19(4) (2014), 1213–1228.
5. Askarzadeh, A. A novel metaheuristic method for solving constrained en-gineering optimization problems: crow search algorithm, Comput. Struct. 169 (2016), 1–12.
6. Balavand, A. A new feature clustering method based on crocodiles hunting strategy optimization algorithm for classification of MRI images, Vis. Comput. (2021) 1–30.
7. Clerc, M., Particle swarm optimization, Vol. 93. John Wiley & Sons, 2010.
8. Coello, C.A.C., Use of a self-adaptive penalty approach for engineering optimization problems, Comput. Ind. 41(2) (2000), 113–127.
9. Coello, C.A.C., Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art, Com-put. Methods Appl. Mech. Eng. 191(11-12) (2002), 1245–1287.
10. Cuevas, E., Cienfuegos, M., Zaldívar, D., and Pérez-Cisneros, M.A swarm optimization algorithm inspired in the behavior of the social-spider, Ex-pert Syst. Appl. 40 (16) (2013), 6374–6384.
11. Dasgupta, D. An overview of artificial immune systems and their appli-cations, in Artificial immune systems and their applications, Artificial immune systems and their applications (1993), 3–21.
12. Dinets, V. Apparent coordination and collaboration in cooperatively hunt-ing crocodilians, Ethol. Ecol. Evol. 27(2) (2015), 244–250.
13. Dorigo, M. and Gambardella, L.M. Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput. 1(1) (1997), 53–66.
14. Duman, E., Uysal, M. and Alkaya, A.F. Migrating birds optimization: A new metaheuristic approach and its performance on quadratic assignment problem, Inform. Sci. 217 (2012), 65–77.

15. Eskandar, H., Sadollah, A., Bahreininejad, A., and Hamdi, M. Water cy-cle algorithm-A novel metaheuristic optimization method for solving con-strained engineering optimization problems, Comput. Struct. 110 (2012),151–166.
16. Eusuff, M., Lansey, K. and Pasha, F. Shuffled frog-leaping algorithm:a memetic meta-heuristic for discrete optimization, Eng. Optim. 38(2)(2006), 129–154.
17. Faramarzi, A., Heidarinejad, M., Mirjalili, S., and Gandomi, A.H. Marine predators algorithm: A nature-inspired Metaheuristic, Expert Syst. Appl. 152 (2020) 113377.
18. Faris, H., Aljarah, I., Al-Betar, M. A., and Mirjalili, S. Grey wolf op-timizer: a review of recent variants and applications, Neural Comput. Appl. 30(2) (2018), 413-435.
19. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., and Chen, H. Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst. 97 (2019), 849–872.
20. Jain, M., Singh, V. and Rani, A. A novel nature-inspired algorithm for optimization: Squirrel search algorithm, Swarm Evol. Comput. 44 (2019), 148–175.
21. José-García, A. and Gómez-Flores, W. Automatic clustering using nature-inspired metaheuristics: A survey, Appl. Soft Comput. 41 (2016), 192–213.
22. Kashan, A.H. An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA), Comput Aided Des. 43(12) (2011), 1769–1792.
23. Kaveh, A. and Farhoudi, N. A new optimization method: Dolphin echolo-cation,Adv. Eng. Softw. 59 (2013), 53–70.
24. Kennedy, J. and Eberhart, R. Particle swarm optimization, in Proceed-ings of ICNN’95-international conference on neural networks. 1995.
25. Kiran, M.S., TSA: Tree-seed algorithm for continuous optimization, Ex-pert Syst. Appl. 42(19) (2015), 6686–6698.
26. Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. Optimization by simulated annealing, Science 220(4598) (1983), 671–680.
27. Li, J.Q., Sang, H.Y., Han, Y.Y., Wang, C.G. and Gao, K.Z. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions, J. Clean. Prod. 181 (2018), 584–598.

28. Lin, L. and Gen, M. Auto-tuning strategy for evolutionary algorithms: bal-ancing between exploration and exploitation, Soft Comput. 13, (2) (2009),157–168.
29. Martin, R. and Stephen, W. Termite: A swarm intelligent routing al-gorithm for mobilewireless Ad-Hoc networks, Stigmergic optimization.Springer, Berlin, Heidelberg, 2006. 155–184.
30. Michalewicz, Z. A Survey of constraint handling techniques in evolution-ary computation methods, Evol Comput. 4 (1995), 135–155.
31. Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl. Based Syst. 89 (2015), 228–249.
32. Mirjalili, S. and Lewis, A. The whale optimization algorithm, Adv. Eng. Softw., 95 (2016), 51–67.
33. Mirjalili, S., Mirjalili, S.M. and Lewis, A. Grey wolf optimizer, Adv. Eng.Softw. 69 (2014), 46–61.
34. Mucherino, A. and Seref. O. Monkey search: A novel metaheuristic search for global optimization, AIP conference proceedings. Vol. 953. No. 1. American Institute of Physics, 2007.
35. Nezhad, A.M., Shandiz, R.A. and Jahromi, A.E. A particle swarm-BFGS algorithm for nonlinear programming problems, Comput. Oper. Res. 40(4) (2013), 963–972.
36. Olorunda, O. and Engelbrecht. A.P. Measuring exploration/exploitation in particle swarms using swarm diversity,In 2008 IEEE congress on evo-lutionary computation (IEEE world congress on computational intelli-gence), pp. 1128–1134. IEEE, 2008.
37. Pan, W.-T. A new fruit fly optimization algorithm: taking the financial distress model as an example, Knowl. Based Syst. 26 (2012), 69–74.
38. Ray, T. and Liew, K.M. Society and civilization: An optimization al-gorithm based on the simulation of social behavior, IEEE Trans. Evol. Comput. 7(4) (2003), 386–396.
39. Sadollah, A., Bahreininejad, A., Eskandar, H., and Hamdi, M. Mine blast algorithm: A new population based algorithm for solving constrained en-gineering optimization problems, Appl. Soft Comput. 13(5) (2013), 2592–2612.
40. Saremi, S., Mirjalili, S. and Lewis, A. Grasshopper optimisation algo-rithm: theory and application, Adv. Eng. Softw. 105 (2017), 30–47.
41. Yang, X.-S., Firefly algorithm, stochastic test functions and design opti-misation,Int. J. Bio-Inspired Comput. 2(2) (2010), 78–84.

42. Yang, X.-S. and Deb. S. Cuckoo search via Lévy flights, In 2009 World congress on nature & biologically inspired computing (NaBIC), pp. 210–214. IEEE, 2009.
43. Yang, X.-S. and He, X. Bat algorithm: literature review and applications, Int. J. Bio-Inspired Comput. 5(3) (2013), 141–149.
44. Yapici, H. and Cetinkaya, N. A new meta-heuristic optimizer: Pathfinder algorithm, Appl. Soft Comput. 78 (2019),  45–568.
45. Zhang, J., Xiao, M., Gao, L. and Pan, Q. Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization prob-lems, Appl. Math. Model. 63 (2018), 464–490.