## 2 Background and Related Work  
Metaheuristic approaches are problem-independent, hit-and-trial-based population search processes to intelligently obtain the optimal and best solutions. Metaheuristic approaches can be distinguished as local search, based on population or random search and constructive [29] or breaking the problem into different sub-problems [30], and are used for multi-point global optimization. PSO is a technique inspired by the coordinated movements of a bird flock. While a bird flies and searches randomly for food, for instance, all birds in the flock can share their discovery and help the flock obtain the best hunt [31]. The SSA mimics the foraging and anti-predation behavior of the sparrow population. The algorithm formulates the search for better food sources by the sparrow population as an optimization algorithm [32]. The WOA mimics the social behavior of humpback whales, drawing inspiration from their unique bubble-net hunting strategy. This foraging behavior involves whales hunting krill or small fish schools near the surface by creating distinctive bubbles along a circular or \"9\"-shaped path [25]. The GWO simulates the leadership hierarchy and hunting behavior of grey wolves in nature categorizing grey wolves into four types, alpha, beta, delta, and omega, to represent the leadership structure [24]. The MPA mimics the foraging behaviors of marine organisms such as sharks, toucans, sunfish, and swordfish while searching for food and follows the foraging strategy of [PERSON] and Brownian movements with an optimal encounter rate policy in marine ecosystems [26]. ALO mimics the hunting mechanism of antlions in nature where ants explore their prey by selecting antlions, random walks of ants, and adapting to shrinking boundaries of antlions [28]. Different scholars have presented innovative ideas about wireless network cellular planning. In [33], base station deployment optimization techniques are investigated in interested areas using Simulated Annealing (SA) for heterogeneous cells in LTE networks. An efficient, optimized cellular planning approach is proposed using the stochastic optimization method in [34] and under demand uncertainty in [35]. A greedy-based algorithm maximizes power-saving indicators of heterogeneous networks in [36]. A comparative study between the Poisson point process and the Poisson hole process is carried out in [37] for pico base stations in a two-tier heterogeneous network, concluding energy efficiency improvement can be achieved based on appropriately selecting the transmit power of pico base stations. Multi-objective optimization algorithms are proposed in [38] considering capacity, coverage, and cost considering interference as a significant constraint, adding local search and decomposition strategy to the genetic algorithm for 4G heterogeneous network and finding the most feasible solutions for Base Transceiver Station (BTS) planning with faster computations. In [39], cellular network planning is carried out for a micro-macro-relay combination scenario considering coverage, capacity, and cost scenario and reusing existing sites in mixed cell structure and concluded that the approach could reduce the cost of Mbps per square km up to 20 times. In [40], the concept of smart grid integration, passive cooling is proposed for central offices, and smart sleep modes for energy efficient green 5G network and beyond are proposed and carried out techno-economic analysis for the migration of existing networks. A joint optimized procedure is proposed for cell planning and fiber back-haul designing problems in [41]. A non-dominated sorting genetic algorithm is proposed to deploy a minimum number of base stations using wired and wireless back-haul systems satisfying cell capacity and coverage and a proposed cost-minimizing approach in a 5G network. Different researchers carried out techno-economic and sensitivity analyses for cost-effective deployment of 5G networks.