Hybrid Firefly Algorithm and Sperm Swarm Optimization Algorithm using Newton-Raphson Method (HFASSON) and its application in CR-VANET

TL;DR

This paper introduces HFASSON, a hybrid optimization algorithm combining FA, SSO, and Newton-Raphson, which outperforms existing algorithms on benchmark functions and improves spectrum utilization in CR-VANET applications.

Contribution

The paper presents a novel hybrid algorithm, HFASSON, integrating FA, SSO, and N-R method, demonstrating superior performance in optimization benchmarks and wireless network spectrum management.

Findings

HFASSON outperforms other algorithms on benchmark functions.

HFASSON achieves better spectrum utilization in CR-VANET.

Statistical tests confirm HFASSON's superior effectiveness.

Abstract

This paper proposes a new hybrid algorithm, combining FA, SSO, and the N-R method to accelerate convergence towards global optima, named the Hybrid Firefly Algorithm and Sperm Swarm Optimization with Newton-Raphson (HFASSON). The performance of HFASSON is evaluated using 23 benchmark functions from the CEC 2017 suite, tested in 30, 50, and 100 dimensions. A statistical comparison is performed to assess the effectiveness of HFASSON against FA, SSO, HFASSO, and five hybrid algorithms: Water Cycle Moth Flame Optimization (WCMFO), Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA), Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA), Grey Wolf and Cuckoo Search Algorithm (GWOCS), and Hybrid Firefly Genetic Algorithm (FAGA). Results from the Friedman rank test show the superior performance of HFASSON. Additionally, HFASSON is applied to Cognitive Radio Vehicular Ad-hoc Networks (CR-VANET), outperforming basic CR-VANET in spectrum utilization. These findings demonstrate HFASSON's efficiency in wireless network applications.