# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 1 Introduction  
The mobile Ad hoc network is a multi-hop temporary autonomous system formed by mobile nodes through a wireless link [1]. The nodes in MANET can dynamically join and leave the network [2]. Due to its flexible network capabilities and the lack of infrastructure, it is widely used in military and civilian applications. The typical application scenario of MANET is to establish communication between rescuers, search teams and medical personnel in the disaster area where network infrastructure is damaged [3]. In addition, it has many other applications in our daily lives, including habitat monitoring, environmental monitoring and ocean monitoring, as well as emerging application areas such as traveler positioning, target tracking and social networking [4; 5; 6; 7; 8; 9]. With the use of close-range space and the rapid development of aircraft such as drones, the mobile ad hoc network scene is more diversified. However, due to the slow progress of battery technology, energy is still a bottleneck for the wider application of MANETs [10]. For energy-limited mobile Ad Hoc networks, the main challenges in the design of routing protocols are not only to ensure global convergence, but also to improve route discovery efficiency. It also needs to maximize the energy consumption of the network, ensure the continuity of data transmission, and automatically adapt to the dynamic changes of the network topology. The existing literature has conducted extensiveresearch on energy equalization protocols. Utilizing an efficient energy approach can minimize energy consumption throughout the network and maximize network life. These protocols can generally be divided into two categories: minimum energy routing protocols and maximum network lifetime routing protocols. The minimum energy routing protocol searches for the most energy-efficient path from the source node to the destination node [11], and the maximum network lifetime routing protocol attempts to balance the residual battery power of each node when searching for energy-saving paths [12]. The ant colony algorithm has good performance in terms of energy balance and has been extensively studied. Bionic algorithms mimic the behavior of living things on Earth. The purpose of evolution is to achieve a given goal and ultimately achieve optimal results. The approach is to encapsulate simple heuristic rules into distributed operations [13]. The ant colony optimization algorithm has many good characteristics. It is a new general heuristic algorithm framework for solving combinatorial optimization problems. It has the characteristics of positive feedback, distributed computing and dynamic adaptation. Since the ant colony optimization algorithm has the above advantages, many researchers are working in this direction [14]. The ant colony algorithm is a recently prevalent global searching algorithm owing to its perfect performance in solving optimization problems [15]. AntNet proposed in [16] is the first routing algorithm based on ant colony optimization. The local model of the network state and the local routing table are updated when the reverse ant original path returns. This provides a basic model for the application of ant colony optimization algorithms to MANET. The literature [17] designed an active routing protocol-probabilistic emergent routing algorithm (PERA). The protocol also uses ant discovery network topology and routing maintenance in dynamic networks for mobile network topologies such as MANET. The improved ant colony optimization routing algorithm for mobile ad hoc networks (PACONET) proposed in the literature [18] uses forward and backward ants to update pheromone, which is different from the AntNet algorithm. The forward ant updates the pheromone to speed up the convergence of the algorithm, but it also brings the problem of being more likely to fall into the local optimum. The forward ant overhead of the broadcast increases because the ant needs to record the total time of the pathfinding and the ID of the passed node.