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.

翻译：本文提出了一种新的混合算法，该算法融合了萤火虫算法（FA）、精子群优化算法（SSO）以及牛顿-拉夫森（N-R）方法，以加速向全局最优解的收敛，并将其命名为基于牛顿-拉夫森法的萤火虫算法与精子群优化混合算法（HFASSON）。HFASSON的性能通过CEC 2017测试集中的23个基准函数进行评估，测试维度分别为30、50和100。通过统计比较，评估了HFASSON相对于FA、SSO、HFASSO以及五种混合算法（水循环蛾焰优化算法（WCMFO）、混合粒子群优化与遗传算法（HPSOGA）、混合精子群优化与引力搜索算法（HSSOGSA）、灰狼与布谷鸟搜索算法（GWOCS）以及混合萤火虫遗传算法（FAGA））的有效性。弗里德曼秩检验的结果表明HFASSON具有优越的性能。此外，HFASSON被应用于认知无线电车载自组织网络（CR-VANET）中，在频谱利用率方面优于基本的CR-VANET。这些发现证明了HFASSON在无线网络应用中的高效性。