Improved spectrum allocation scheme for TV white space networks using a hybrid of firefly, genetic, and ant colony optimization algorithms

Abstract

This study proposes a novel hybrid Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm (FAGAACO) for spectrum allocation in TV White Space (TVWS) networks. The Genetic Algorithm (GA) was used in the design to provide cross-over chromosomes to both the Firefly Algorithm (FA) and the Ant Colony Optimization Algorithm (ACO), thereby improving the exploration abilities of FA and ACO and preventing FA and ACO from becoming trapped in local optimum. The proposed algorithm was implemented using MATLAB R2018a. Simulation results show that in comparison with a hybrid of the Firefly Algorithm and Genetic Algorithm (FAGA), the proposed algorithm achieved 13.03% higher throughput, 1.3% improved objective function value and 5.03% higher runtime due to the good accuracy of the proposed algorithm. Based on these improvements, the proposed algorithm is therefore an efficient spectrum allocation technique in TVWS networks.

Keywords

• Local optimum
• Spectrum allocation
• TVWS
• Throughput