# Routing Enhancement in MANET Using Particle Swarm Algorithm
## 2 Literature Review
An optimized particle swarm algorithm (OPSO) integrated into the location-aided routing (LAR) protocol to enhance the energy consumption in MANET was proposed in [12]. The OPSO improves the exploration and exploitation process in the energy-aware location-aided algorithm (EALAR) protocol by replacing a nonuniform mutation with a uniform mutation using PSO. Network performance including packet delivery rate and power consumption was enhanced by combining the OPSO algorithm into the LAR protocol, while a high E2E delay was achieved. [PERSON] and [PERSON] in [13] used a hybrid swarm optimization model (CPSO) to improve routing protocols in MANET. They combined PSO with cat swarm optimization (CSO). The model tried to enhance the locally optimal solution known as stagnation by expanding the possibilities of the metaheuristic algorithm. The hybrid model offered proposed solutions for populations. The proposed method first searches for a local solution; if it has an unsuccessful search, it will use the PSO immediately. If the PSO is already at the local optimal and the possible solution did not achieve any enhancement, it will use the cat swarm optimization. The objective function considers latency and packet loss, and PDR increases the network performance. The authors of [14] introduced a clustering and routing protocol using PSO (CRPL) to select the appropriate next-hop relay nodes of CHs to decrease the consumption power and increase the network lifetime. The system used sensor nodes in a target area to form clusters managed by Cluster Heads (CHs). It used PSO to set the cluster head members (CHs) and select the best path for CHs. The network functions under assumptions like static and homogeneous nodes, symmetric communication links, and a transmission power that is modified according to distance. It employs different energy models for different distances to enhance network efficiency and lifetime. The CRPL reduced the energy consumption during routing. However, its fitness function with the CRPL considers only CHs and their associated relay nodes, decreasing energy efficiency. The authors of [15] proposed a Binary Particle Swarm Optimization algorithm (BPSO) to improve the energy in the TORA protocol. They developed a new fitness function based on node energy among all possible routing nodes and hops numbers in the selected path. BPSO maximized network lifetime and enhanced network performance. However, the evaluation did not consider energy efficiency and its important parameters. [PERSON] et al. in [16] integrated an expected transmission count (ETX) with PSO. To choose the appropriate paths with the minimum number of hops, the proposed method employs the ETX to determine the number of transmissions required for a packet to be properly transmitted over a connection. Moreover, the PSO algorithm prioritizes paths based on fitness values to reduce the packet transmission cost. The proposed PSO-ETX achieved high efficiency in delivering the packet but did not work effectively with a greater number of hops. A Particle Swarm Optimization model dependent on On-Demand Multicast Routing Protocol (PSO-ODMRP) was proposed in [17]. The PSO-ODMRP was intended to enhance the multicast routing optimization algorithm in MANETs. The PSO-ODMRP builds a multicast tree periodically through flooding control packets, and nodes that want to join the multicast group respond accordingly. The algorithm achieved high performance in low mobility, but under a high mobile density, the performance decreased. The authors of [18] suggested a stable routing method using PSO and centrality betweenness-based stability (CBS) to select a proper network path. The PSO-CBS determines the stable path by choosing nodes based on link quality, remaining energy, and node degree. The proposed method increases the packet delivery ratio and decreases the network delay and the congestion rate. A Predictive Energy Efficient and Reliable Multicast Routing method utilizing the PSO algorithm was introduced in [19].