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Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

Nitin Mittal1, Rohit Salgotra2,3, Abhishek Sharma4, Sandeep Kaur5, S. S. Askar6, Mohamed Abouhawwash7,8,*

1 University Center for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India
2 Faculty of Physics and Applied Computer Science, AGH University of Science & Technology, Krakow, Poland
3 Faculty of Information Technology, Middle East University, Amman, 11813, Jordan
4 Department of Computer Engineering and Applications, GLA University, Mathura, 281406, India
5 Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, 143005, India
6 Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
7 Department of Computational Mathematics, Science and Engineering, College of Engineering, Michigan State University, East Lansing, MI, 48824, USA
8 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt

* Corresponding Author: Mohamed Abouhawwash. Email: email

Intelligent Automation & Soft Computing 2023, 37(3), 3159-3177. https://doi.org/10.32604/iasc.2023.041059

Abstract

The optimization of cognitive radio (CR) system using an enhanced firefly algorithm (EFA) is presented in this work. The Firefly algorithm (FA) is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies. It has already proved its competence in various optimization problems, but it suffers from slow convergence issues. To improve the convergence performance of FA, a new variant named EFA is proposed. The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions, and simulation results show its superior performance compared to biogeography-based optimization (BBO), bat algorithm, artificial bee colony, and FA. As an application of this algorithm to real-world problems, EFA is also applied to optimize the CR system. CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem. However, it requires optimization to meet specific performance objectives. The results obtained by EFA in CR system optimization are compared with results in the literature of BBO, simulated annealing, and genetic algorithm. Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

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APA Style
Mittal, N., Salgotra, R., Sharma, A., Kaur, S., Askar, S.S. et al. (2023). Optimization of cognitive radio system using enhanced firefly algorithm. Intelligent Automation & Soft Computing, 37(3), 3159-3177. https://doi.org/10.32604/iasc.2023.041059
Vancouver Style
Mittal N, Salgotra R, Sharma A, Kaur S, Askar SS, Abouhawwash M. Optimization of cognitive radio system using enhanced firefly algorithm. Intell Automat Soft Comput . 2023;37(3):3159-3177 https://doi.org/10.32604/iasc.2023.041059
IEEE Style
N. Mittal, R. Salgotra, A. Sharma, S. Kaur, S.S. Askar, and M. Abouhawwash "Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 3159-3177. 2023. https://doi.org/10.32604/iasc.2023.041059



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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