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

Rehannara Beegum T, Mohd Yamani Idna Idris, Mohamad Nizam Bin Ayub, Hisham A Shehadeh, Usman Ali

TL;DR

HFASSON introduces a novel hybrid optimization framework that fuses the exploration strengths of the Firefly Algorithm with the exploitation strengths of Sperm Swarm Optimization, augmented by Newton-Raphson refinement to accelerate convergence toward global optima. Evaluated on 23 CEC 2017 benchmark functions across 30, 50, and 100 dimensions, HFASSON outperforms FA, SSO, HFASSO, and five hybrid methods according to Friedman statistical analysis, achieving rapid convergence and frequent attainment of optimum values. The authors extend HFASSON to a CR-VANET spectrum sensing problem, demonstrating improved spectrum utilization over a baseline CR-VANET approach across multiple vehicle counts, thus validating practical effectiveness in wireless networks. Overall, HFASSON offers a robust, fast-converging optimization tool with solid empirical evidence for both standard benchmarks and a real-world application in cognitive radio-enabled vehicular networks.

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.

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

TL;DR

HFASSON introduces a novel hybrid optimization framework that fuses the exploration strengths of the Firefly Algorithm with the exploitation strengths of Sperm Swarm Optimization, augmented by Newton-Raphson refinement to accelerate convergence toward global optima. Evaluated on 23 CEC 2017 benchmark functions across 30, 50, and 100 dimensions, HFASSON outperforms FA, SSO, HFASSO, and five hybrid methods according to Friedman statistical analysis, achieving rapid convergence and frequent attainment of optimum values. The authors extend HFASSON to a CR-VANET spectrum sensing problem, demonstrating improved spectrum utilization over a baseline CR-VANET approach across multiple vehicle counts, thus validating practical effectiveness in wireless networks. Overall, HFASSON offers a robust, fast-converging optimization tool with solid empirical evidence for both standard benchmarks and a real-world application in cognitive radio-enabled vehicular networks.

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.

Paper Structure

This paper contains 16 sections, 21 equations, 2 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Rudimentary study of FA, SSO and Newton-Raphson Method
  3. Standard FA and SSO algorithms
  4. FA
  5. SSO
  6. Newton-Raphson Method (N-R Method)
  7. Hybrid Firefly Algorithm and Sperm Swarm Optimization with Newton's Method (HFASSON)
  8. Experimental Results
  9. Statistical Analysis
  10. Application of the proposed Algorithm
  11. Background and problem formulation
  12. Energy Detection Method
  13. Objective function
  14. Result Analysis of CR-VANET_HFASSON
  15. Spectrum Utilization Vs Vehicle Count (VC) Analysis
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Spectrum sensing techniques10.1016/j.comnet.2006.05.001.
  • Figure 2: Variation of Pfa and Pd in ROC curve with range of SNR from 17dB to -17dB for spectrum sensing using energy detection model.