## 2 Energy Efficiency (EE) Studies in LAR of Ad Hoc Networks
Nature-inspired algorithms have two basic forms: evolutionary algorithms (EA) and swarm-based algorithms. EAs are inspired by the natural concept of evolution [32]. They are a subset of evolutionary computations, a generic metaheuristic (population-based) that is contrived to solve problems with high complexity [33], such as nondeterministic polynomial-time (NP-hard) problems [34]. Several studies use metaheuristic to solve the problems of routing protocols in MANETs. This algorithm can be used to solve problems that require the systematic enumeration of all potential candidates for reaching the global optimal can also be solved by using this algorithm [35]. Metaheuristic algorithms are inspired by nature as an efficient way to deal with various complex searching and optimization problems in different fields. PSO is a popular and state-of-the-art algorithm proposed by [36]. It is inspired by the behavior of bird flocks; in particular, PSO applied to MANETs appears like a difficult mechanism, but it has direct calculations to find the exact route leading to the destination. The procedure of PSO depends on the swarm that is taking the same behavior as the nodes. During the searching procedure in the solution space, PSO runs through some iterations that are determined with parameters to find the best route, which is near the destination node to send the data at a low cost. The node in the network of MANETs can act as the sender, receiver, and route at the same time. In addition, it can change its position from place to other [37]. Some studies have tackled the limitation of MANETs [38] and proposed an Extensible PSO (EPSO) model to find the reliable routing path in accordance with some rules. The battery level should have enough energy to forward the packet and the data continuously. EPSO starts operating by searching the possible routes for the sender and receiver nodes in the network and checking several routes to enable EPSO. Improving the possible route this step prepared allows the algorithm to choose the route set to revive the load-controlling path and increases the network lifetime and efficiency. In [39], the Cuckoo Search (CS) algorithm was implemented by enhancing the conventional CS technique using the reactive routing Cuckoo Search Optimization AODV (CSOAODV) protocol. This study aimed to ensure the QoS path by calculating the best fitness value because whether or not the short path can be the optimal Route Replay (RRPLY) packet of the AODV protocol has no guarantee. The model modifies the default setup of the AODV protocol to calculate the best path by the number of hubs from the source to the destination. The lowest number of hubs is considered as the path, whereas CSOAODV considers best fitness as value computation. The best fitness value is determined with three criteria: calculated residual energy, hop count, and routing load. In the same protocol, [PERSON]. [32] used the shuffled frog-leaping algorithm with the AODV protocol and the proposed model named frog-leaping algorithm-based ad hoc on-demand distance vector (SEAL-AODV). SEAL computes the optimal path for AODV based on the following criteria: residual energy, hop count, and routing load of [39] but different algorithms. [PERSON] [40] proposed a model to enhance the performance of dynamic source routing (DSR) using the ant colony optimization algorithm, to achieve high packet delivery ratio, low E2E delay, and low overhead cost when the source node wants to send RREQ. All previous achievements ensure the energy consumption will be minimized in the = MANETs. In the enhanced-ant-DSR algorithm, the source node is required to send a packet for the destination first step to check the cache. When no routes are well known, the source node broadcasts the route request control packets locally called by (Req.Ant packets) to find a route. When the RREQ (Req.Ant packets) reach the destination node, its forward route reply control packet contains route information of the source node through the same route.