Open Access

ARTICLE

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

by Nitin Mittal¹, Rohit Salgotra^2,3, Abhishek Sharma⁴, Sandeep Kaur⁵, S. S. Askar⁶, Mohamed Abouhawwash^7,8,*

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:

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

Received 09 April 2023; Accepted 17 June 2023; Issue published 11 September 2023

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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