In this paper, a highly adaptive multi-objective optimization framework is proposed for the optimal positioning of 5G base stations in different cellular networks, such as Urban Macro (UMa), Urban Micro (UMi), and Rural Macro (RMa). The optimization framework is built upon an adaptive version of the Moth Flame Optimization Algorithm (AMFO) which provides robust solutions in different network scenarios by managing complex requirements of mm-Wave-based 5G network. The performance of the proposed algorithm is compared with several other state-of-art metaheuristic algorithms. Comparative analysis shows that the proposed AMFO algorithm significantly outperforms other algorithms for all network scenarios by properly maintaining the network challenges and yielding optimal results with the lowest computational time. The adaptability of the proposed algorithm for dynamic environments and propagation characteristics makes it a great candidate solution for diverse practical 5G network deployment.
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