Beam selection is a major conflict in wireless communications. In the existing methods, the energy efficiency maximization problem is handled by the non-convex fractional programming algorithm. Yet, various traditional works have used the Single Beam Selection (SBS) scheme having the mmWave beamspace Multi-Input Multi-Output (MIMO) channel for minimizing the MIMO dimension. Still, multiple Radio-Frequency (RF) chain groups cannot be properly selected by users. Generally, the conventional (SBS) concept cannot work with a multiple beam group selection opportunities for all the types of supported users. So, the user suffers from a severe computational burden for measuring the decoded message and it also leads to a beam optimization problem. This paper plans to integrate the two meta-heuristic algorithms like Beetle swarm optimization (BSO) and Grey Wolf Optimization called the Alternative Grey Wolf with Beetle Swarm Optimization (AGW-BSO) for developing the Hybrid Beam Selection (HBS) scheme in MIMO-NOMA and the new HBS scheme could support the multiple SBS scheme in beamspace MIMO-NOMA systems. Here, the optimization of RF chain group by the proposed AGW-BSO is considered as the main contribution, in such a way to attain the multi-objective function concerning the maximization of beam power, energy efficiency, and spectral efficiency. Finally, the attained objective of the proposed model is analyzed by comparing the proposed model over the conventional models through computer simulations.

波束选择是无线通信中的一个主要冲突。在现有的方法中，能量效率最大化问题是通过非凸分数规划算法来处理的。然而，各种传统工作已经使用具有毫米波波束空间多输入多输出 (MIMO) 信道的单波束选择 (SBS) 方案来最小化 MIMO 维度。尽管如此，用户仍无法正确选择多个射频（RF）链组。通常，传统 (SBS) 概念无法为所有类型的受支持用户提供多波束组选择机会。因此，用户要承受测量解码消息的严重计算负担，并且还会导致波束优化问题。本文计划集成两种元启发式算法，如甲壳虫群优化 (BSO) 和灰狼优化，称为甲虫群优化的替代灰狼 (AGW-BSO)，用于开发 MIMO-NOMA 中的混合波束选择 (HBS) 方案并且新的 HBS 方案可以支持波束空间 MIMO-NOMA 系统中的多个 SBS 方案。在这里，提出的 AGW-BSO 对 RF 链组的优化被认为是主要贡献，以这种方式实现关于波束功率、能量效率和频谱效率最大化的多目标函数。最后，通过计算机模拟将所提出的模型与传统模型进行比较，分析所提出的模型所达到的目标。