# Routing Enhancement in MANET Using Particle Swarm Algorithm
## 2 Literature Review
The PEERMR builds an energy-efficient multicast tree using PSO. This approach builds an energy-efficient multicast tree by evaluating a fitness function that takes into account the delay, energy efficiency, and path stability during simultaneous data transmission to multiple destinations. The study assumes a planar Poisson distribution for node deployment within a defined area and focuses on metrics such as residual energy, delay, and link expiration time for routing decisions. The PEERMR maintained a higher delivery ratio and lower latency than existing methods. However, compared to other nodes, a significant drawback is the overloading of large root nodes. The study in [20] suggested a Fixed-PSO route selection technique using PSO for MANETs. The algorithm focuses on minimizing the distance from source to destination node. The Fixed-PSO approach allows for effective route discovery in dynamic networks where nodes can join or leave unexpectedly. Parameters considered in the fitness function are the total distance from the source node to the destination and the distance between the last node in the path to the destination node. Two simulation scenarios were conducted: one under the normal situation (all nodes connected) and another under the abnormal condition (some nodes disabled). In normal conditions, the minimum distance for route selection was achieved. According to the results in abnormal situations, additional relay nodes were required. There are different Artificial intelligence (AI) algorithms used to enhance MANET routing. Deep reinforcement learning (DRL) is one of the popular artificial intelligence methods that combines deep neural networks and reinforcement learning. DRL contains states, actions, and rewards. The agent interacts with its environment to assess its state and produce the best action that maximizes the reward. The DRL does not need prior knowledge of the mathematical models; it learns by interacting with its environment. It allows agents to learn complex strategies by interacting with their environment iteratively and making decisions that maximize the rewards. The authors of [21] present a routing protocol called DeepCQ+ integrating multi-agent deep reinforcement learning techniques with Q-learning-based routing protocols for MANETs. The proposed routing algorithm optimizes next-hop selection while using acknowledgment (ACK) packets to share limited information between network nodes and to encourage minimal overhead in networks. The proposed algorithm achieves better end-to-end throughput with lower overhead compared to traditional Q-learning-based protocols. The authors of [22] integrated an Artificial Neural Network (ANN) with the Link State Routing Protocol (LSR) to optimize the LSR. The Optimized Link State Routing Protocol (OLSR) works on limited variable attributes in the MANET. Since MANETs are dynamic environments, the default variablesof the attribute are not proper. Therefore, the authors integrated the OLSR with an ANN to adjust the OLSR's configuration attributes. The proposed method decreases power consumption and maximum PDR. However, the proposed algorithm is inefficient in large networks due to the high computational process. Most previous studies are not efficient in large networks and do not preform well with a greater number of hops. Additionally, they do not consider important parameters that affect network performance, such as delay. To overcome these limitations, we propose a new particle swarm optimization algorithm that can improve routing in MANETs and enhance their performance. The EPSO algorithm selects the stable path by considering multiple metrics such as short distance, path delay, and energy consumption. Metrics such as throughput, PDR, number of valid paths, and E2E delay are utilized for analyzing the performance of the proposed PSO against the other existing algorithms.