## 2 Related Work  
Related literature emphasizes pertinent and contemporary approaches, as well as their positive and negative aspects. For optimum routing, the heterogeneous multidepot cooperative vehicle-routing problem (HMCVRP) is suggested [19]. The suggested technique outperformed the CPLEX solver, which employs the commonly used big-M transformation-based approach, in numerical tests. Further computer studies demonstrated the importance of depot locations and the implementation of an efficient cost-saving distribution system. The cuckoo-optimization algorithm was used by the authors of [20] to show energy-aware clustering-based routing (ECR) in wireless-sensor networks. The present study suggests an energy-conscious bunching-based guiding convention for wireless-sensor networks that can group the system and pick optimal cluster leaders using cuckoo-improvement computation. The suggested approach considers four criteria for choosing cluster leaders in the focused cuckoo computation, specifically, the remaining energy of the node, isolation from the base sink, inside-group separations, and amid-group separations. The suggested approach outperformed various calculations in MATLAB circumstances, such as low-energy adaptive-clustering-hierarchy (LEACH)-application explicit low-power directing, and low-energy adaptive-clustering-hierarchy energy prediction (LEACH-EP), with the parting-based edge as the core node dies by the large and packet rate of transmission in six different situations. The authors proposed an improved basis for the ACO algorithm based on fuzzy logic (ACO-FL) for vehicular ad hoc networks (VANETs) [21]. To test the efficacy and speed of the proposed protocol, the network simulator NS-2 was used. The simulation findings indicated that compared to current routing algorithms such as the ad hoc on-demand distance vector (AODV) and ACO, the proposed protocol achieved a reduced end-to-end delay and a greater data-packet delivery ratio. This proposed routing protocol establishes a hierarchical routing scheme in which nodes in the outer level forward data to nodes in the inner level [22]. Ant colonies are used to improve the routing route, ensuring that data travel on the least congested path feasible. Furthermore, when ant-colony optimization is used, certain cluster-head nodes may become overwhelmed with data forwarding, leading to early death due to an energy shortage. The quantity of data from the nearby cluster head is evaluated and transmitted based on their extra energy to surmount this anomaly. The authors concluded that the system saves more energy, prolonging the lifespan of the network, after comparing the energy-usage results of the proposed routing using ant-colony optimization (RACO) with those of other existing clustering algorithms. A prediction approach was added to the current metaheuristic methodology [23]. A refined metaheuristic method is called ant-colony optimization. Instead of encouraging ants to seek higher pheromone densities, the suggested methodology forecasts pheromone characteristics based on middle pheromone densities. The intermediate stage's variable pheromone quantities are simulated using the quantum approach. The stochastic differential equation and Ito integral are used to determine the pheromone features outlined below. The predicted pheromones function as parameters in subsequent ACO iterations. To verify the efficiency of the suggested architecture, shortest-route generation is provided for several large-scale networks. The latter is an extremely efficient metaheuristic paradigm that combines quantum-mechanism-based prediction with previously developed metaheuristics. For data transfer, the authors proposed AODV and ACO (AODV-ACO) [24]. The ant-colony-optimization method was used, which considers the initial coordinates of each node. The reactive-routing-protocol route had a high probability of creating network congestion and used substantial network bandwidth.