Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

作     者：Nitin Mittal Rohit Salgotra Abhishek Sharma Sandeep Kaur SSAskar Mohamed Abouhawwash 

作者机构：University Center for Research and DevelopmentChandigarh UniversityMohaliPunjab140413India Faculty of Physics and Applied Computer ScienceAGH University of Science&TechnologyKrakowPoland Faculty of Information TechnologyMiddle East UniversityAmman11813Jordan Department of Computer Engineering and ApplicationsGLA UniversityMathura281406India Department of Computer Engineering&TechnologyGuru Nanak Dev UniversityAmritsarPunjab143005India Department of Statistics and Operations ResearchCollege of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Computational MathematicsScience and EngineeringCollege of EngineeringMichigan State UniversityEast LansingMI48824USA Department of MathematicsFaculty of ScienceMansoura UniversityMansoura35516Egypt 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第9期

页      面：3159-3177页

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 

基　　金：funded by King Saud University,Riyadh,Saudi Arabia.Researchers Supporting Proiect Number(RSP2023R167) King Saud University,Riyadh,Saudi Arabia. 

主　　题：Firefly algorithm cognitive radio bit error rate genetic algorithm simulated annealing biogeography-based optimization 

摘      要：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 prob-lems,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.