Anti-interference Technology of Intelligent Communication Based on Improved GA and GWO

Abstract

Chaotic mapping enhances anti-interference in frequency hopping communication by optimizing genetic algorithm population initialization. An intelligent decision engine model employs optimized grey wolf parameters and individual exchange mechanisms, enhancing grey wolf optimization convergence speed for constructing frequency hopping patterns. Experimental data reveals the improved genetic algorithm's efficiency with average run times of 0.312s and 0.057s for adaptive and enhanced versions, respectively. Achieving optimal solution convergence rates of 99.3% and 100%, the enhanced algorithm boosts decision-making accuracy and efficiency. The intelligent decision engine exhibits strong anti-interference capabilities, suitable for −2dB to 10dB signal-to-interference ratios with error rates below 10⁻⁶. The improved grey wolf optimization algorithm surpasses traditional approaches, yielding a 9.5% profit increment with a total value of 2310. This technology showcases adept learning and anti-interference capabilities, offering innovative solutions for communication systems and anti-interference technology advancements.