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Comparative Study of Prey Predator Algorithm and Firefly Algorithm

Hong Choon Onga, Surafel Luleseged Tilahunb, Wai Soon Leea, Jean Meadard T. Ngnotchouyeb

a School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia;
b School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, South Africa

* Corresponding Author: Surafel Luleseged Tilahun, email

Intelligent Automation & Soft Computing 2018, 24(2), 359-366.


Metaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in search mechanisms of these algorithms it will also help to set criteria on when to use these algorithms. In this paper a comparative study of prey predator algorithm and firefly algorithm will be discussed. The discussion will also be supported by simulation results on selected twenty benchmark problems with different properties. A statistical analysis called Mann—Whitney U 2 test is used to compare the algorithms. The theoretical as well as simulation results support that prey predator algorithm is a more generalized search algorithm, whereas firefly algorithm falls as a special case of prey predator algorithm by fixing some of the parameters of prey predator algorithm to certain values.


Cite This Article

H. C. Ong, S. L. Tilahun, W. S. Lee and J. M. T. Ngnotchouye, "Comparative study of prey predator algorithm and firefly algorithm," Intelligent Automation & Soft Computing, vol. 24, no.2, pp. 359–366, 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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