# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 2 Background and Motivation
### What's the Roles of Bioinspired Algorithms in FANETs? A clustering method comprises virtual sets, which are represented by clusters. Each cluster consists of cluster nodes (CN) or cluster members, each contributing to the selection or suggestion of a CH [15]. A CH's neighbors' nodes within the CH's transmission range are considered cluster members. In theory, any cluster node can be nominated as the cluster head. However, some characteristics may be more critical for a CH than others [20; 21]. These characteristics are considered when selecting a CH. Using the example, an SN with an additional 5G connection is preferred to become a CH than all nodes that do not have this characteristic [21; 22; 23; 24]. The transmission range of the nodes controls the size of a cluster and, as a result, the outcome varies from cluster to cluster [22]. The term \"optimization\" refers to improving performance by efficiently using specific limited resources that exist. During the previous several years, the difficulties associated with solving specific issues have increased, resulting in the need for novel optimization strategies to handle a problem efficiently. The methods were theoretically modelled before developing heuristic optimization algorithms to get the desired results. The most common drawback with mathematical optimization approaches is that local optima tend to become stuck in place [21; 22; 25; 26]. This makes them exceedingly ineffective in resolving real-world problems due to their behaviors. Population-based algorithms, based on randomization, include two critical steps for obtaining improved results: solution development (finding the optimal answer) and exploration (finding alternate solutions). The MFO method is an innovative nature-inspired optimization technique that may be used to address challenging optimization issues. Transverse orientation mimics the movement of moths in nature [22]. Our proposed algorithm uses moths and flames techniques, by which it generates efficient solutions. Moreover, it has been proven that this algorithm can outperform other meta-heuristic optimization techniques in terms of performance. It takes its inspiration from transverse orientation, which is the term used to describe the maritime strategy of moths [20]. Keeping a steady angle concerning the moon's movement allows a moth to hover, as seen in Figure 5. This method is quite effective in drifting over long distances with a straight track. Quite the opposite, artificial lighting can occasionally fool moths [27]. As a result, in comparison to the moon, a manufactured light is very close. While maintaining an equivalent viewpoint to the light, a moth may begin to travel in a spiral route with the light source, which can be disastrous for it [7; 8; 11]. This technique has many advantages, but two stand out. First and foremost, MFO avoids the problem of local optima stagnation [12]. However, some other optimization methods, such as the genetic algorithm (GA) [7; 8; 9; 10], continue to suffer from this problem. Second, MFO offers tremendous potential for exploitation and exploration, enabling it to outperform other processes in the long run. However, even though FANETs are part of ad-hoc networks, they cannot use the same MANET and VANET clustering methods due to their unique characteristics. Instead, new techniques need to be made, or existing practices need to be changed to consider the unique features of a UAV network. UAVs were grouped into clusters, each of which had a specified number of UAVs and one of which was designated as a CH as by [PERSON] et al. [7]. Neighboring UAVs start by exchanging information about their node's specifications. Node information messages include a \"zone ID\" field that categorizes each UAV in a particular cluster. Each node keeps a database of connection quality information in the cluster, including information about its neighbors' distance, SNR, and latency.