FiWi network is a multi-domain network that integrates optical and wireless networks. Hybrid fiber-wireless (FiWi) access network endeavours at consolidating the huge amount of available bandwidth of optical networks and the ubiquity, mobility of wireless access networks with the motive of reducing cost and complexity. This manuscript entails investigations undertaken towards optimal placement of multiple Optical Network Units (ONUs) in FiWi network using two recent optimization algorithms named as Harris Hawks Optimization (HHO) and Grasshopper Optimization algorithms (GOA). The results of the investigations are then benchmarked with respect to the Whale Optimization algorithm (WOA). The outcomes demonstrate the superiority of the proposed HHO algorithm over GOA and WOA algorithms, and return the lowest value of cost function; WOA outperforms GOA in terms of improved convergence rate and time complexity. Additionally, the diversification and intensification features of HHO, GOA and WOA have been compared. In order to benchmark the performance of the HHO and GOA optimizers, a series of convergence curves corresponding to different values of controlling parameters are plotted, and their optimal values determined. The dependence of objective function value upon distribution of users and initial placement of ONUs is also studied; the random placement of users and deterministic placement of ONUs return the lowest value of objective function.

中文翻译：

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使用 Grasshopper 和 Harris Hawks 优化算法优化光纤无线 (FiWi) 接入网络中的多个 ONU 放置

FiWi 网络是一种集光网络和无线网络于一体的多域网络。混合光纤无线 (FiWi) 接入网络致力于整合光网络的大量可用带宽和无线接入网络的普遍性、移动性，其动机是降低成本和复杂性。这份手稿需要使用两种名为 Harris Hawks 优化 (HHO) 和 Grasshopper 优化算法 (GOA) 的最新优化算法，对 FiWi 网络中多个光网络单元 (ONU) 的最佳放置进行调查。然后根据鲸鱼优化算法 (WOA) 对调查结果进行基准测试。结果证明了所提出的 HHO 算法优于 GOA 和 WOA 算法，并返回了成本函数的最低值；WOA 在提高收敛速度和时间复杂度方面优于 GOA。此外，还比较了 HHO、GOA 和 WOA 的多样化和集约化特征。为了对 HHO 和 GOA 优化器的性能进行基准测试，绘制了一系列对应于不同控制参数值的收敛曲线，并确定了它们的最佳值。还研究了目标函数值对用户分布和ONU初始放置的依赖性；用户的随机放置和ONU的确定性放置返回目标函数的最小值。绘制了一系列对应于不同控制参数值的收敛曲线，并确定了它们的最佳值。还研究了目标函数值对用户分布和ONU初始放置的依赖性；用户的随机放置和ONU的确定性放置返回目标函数的最小值。绘制了一系列对应于不同控制参数值的收敛曲线，并确定了它们的最佳值。还研究了目标函数值对用户分布和ONU初始放置的依赖性；用户的随机放置和ONU的确定性放置返回目标函数的最小值。