Implementation Analysis of Cuckoo Search for the Benchmark Rosenbrock and Levy Test Functions

Authors

  • Julius Beneoluchi Odili Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang, Malaysia

DOI:

https://doi.org/10.32890/jict2018.17.1.8243

Keywords:

Cuckoo search, iteration, Levy function, population, Rosenbrock function

Abstract

This paper presents the implementation analysis of the benchmark Rosenbrock and Levy test functions using the Cuckoo Search with emphasis on the effect of the search population and iterations count in the algorithm’s search processes. After many experimental procedures, this study revealed that deploying a population of 10 nests is sufficient to obtain acceptable solutions to the Rosenbrock and Levy test functions (or any similar problem to these test landscapes). In fact, increasing the search population to 25 or more nests was a demerit to the Cuckoo Search as it resulted in increased processing overhead without any improvement in processing outcomes. In terms of the iteration count, it was discovered that the Cuckoo Search could obtain satisfactory results in as little as 100 iterations. The outcome of this study is beneficial to the research community as it helps in facilitating the choice of parameters whenever one is confronted with similar problems.

 

References

Agrawal, S., Panda, R., Bhuyan, S., & Panigrahi, B. K. (2013). Tsallis entropybased optimal multilevel thresholding using cuckoo search algorithm. Swarm and Evolutionary Computation, 11, 16-30. Anwar, S., Inayat, Z., Zolkipli, M. F., Zain, J. M., Gani, A., Anuar, N. B.,Chang,

V. (2017). Cross-VM cache-based side channel attacks and proposed prevention mechanisms: A survey. Journal of Network and Computer Applications.

De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems. (Doctoral dissertation, University of Michigan). Dissertation Abstracts International, 36(10), 5140B. (University Microfilms No. 769381) Journal of ICT, 17, No. 1 (Jan) 2018, pp: 17–

Dorigo, M., & Gambardella, L. (2016). Ant-Q: A reinforcement learning approach to the traveling salesman problem. Paper presented at the Proceedings of ML-95, Twelfth Intern. Conf. on Machine Learning.

Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66-72.

Kamat, S., & Karegowda, A. (2014). A brief survey on cuckoo search applications. Int. J. Innovative Res. Comput. Commun. Eng, 2(2).

Kennedy, J. (2011). Particle swarm optimization. Encyclopedia of Machine Learning (pp. 760-766): Springer.

Khompatraporn, C., Pintér, J. D., & Zabinsky, Z. B. (2005). Comparative assessment of algorithms and software for global optimization. Journal of Global Optimization, 31(4), 613-633.

Odili, J. B. (2013a). Application of ant colony optimization to solving the traveling salesman’s problem. Science Journal of Electrical & Electronic Engineering, 2013.

Odili, J. B. (2013b). Staff development programs and job performance: Implications for productivity in Lagos state ministry of education. Journal of Business Administration and Management Sciences Research, 2(12), 330-334.

Odili, J. B., & Kahar, M. N. M. (2015). African buffalo optimization (ABO): A new meta-heuristic algorithm. Journal of Advanced & Applied Sciences, 3, 101-106.

Odili, J. B., Kahar, M. N. M., & Anwar, S. (2015). African buffalo optimization: A swarm-intelligence technique. Procedia Computer Science, 76, 443448.

Odili, J. B., & Mohmad Kahar, M. N. (2016). African buffalo optimization approach to the design of PID controller in automatic voltage regulator system. National Conference for Postgraduate Research, Universiti Malaysia Pahang, September, 2016, 641-648.

Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. Paper presented at the Evolutionary Computation, 1999.

CEC 99. Proceedings of the 1999 Congress on. Journal of ICT, 17, No. 1 (Jan) 2018, pp: 17–

Taheri, J., Lee, Y. C., Zomaya, A. Y., & Siegel, H. J. (2013). A bee colonybased optimization approach for simultaneous job scheduling and data replication in grid environments. Computers & Operations Research, 40(6), 1564-1578.

West, P., Sculli, G., Fore, A., Okam, N., Dunlap, C., Neily, J., & Mills, P. (2012). Improving patient safety and optimizing nursing teamwork using crew resource management techniques. Journal of Nursing Administration, 42(1), 15-20.

Yang, D., Jiao, J. R., Ji, Y., Du, G., Helo, P., & Valente, A. (2015). Joint optimization for coordinated configuration of product families and supply chains by a leader-follower Stackelberg game. European Journal of Operational Research, 246(1), 263-280.

Yang, X.-S. (2012a). Flower pollination algorithm for global optimization. Paper presented at the International Conference on Unconventional Computing and Natural Computation.

Yang, X.-S. (2012b). Nature-inspired metaheuristic algorithms: Success and new challenges. arXiv preprint arXiv:1211.6658.

Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights. Paper presented at the Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on Nature & Biologically Inspired Computing.

Downloads

Published

26-12-2017

Issue

Vol. 17 No. 1 (2018): Journal of Information and Communication Technology (JICT) Vol.17, No.1, January 2018

Section

Articles

How to Cite

Odili, J. B. (2017). Implementation Analysis of Cuckoo Search for the Benchmark Rosenbrock and Levy Test Functions. Journal of Information and Communication Technology, 17(1), 17-32. https://doi.org/10.32890/jict2018.17.1.8243

Research impact

Harvested 2026-09-08
3 citations, from OpenAlex — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jict2018.17.1.8243 OpenAlex W3017071307