###### Abstract
Similarly, the University of Southern California's [PERSON] team's indoor dense formation experiment with 49 UAVs mostly relies on the airframe to perform the majority of processing tasks, sensor fusion, and trajectory planning. The formation flight also utilizes the virtual structure method [18]. The Vicsek model, which aims to facilitate coordinated control of clusters in constrained space, is an essential multi-intelligence motion control model. It enabled the orderly construction of 30 rotary-wing UAVs outdoors with a top speed of 8 m/s [19; 20]. Furthermore, the US Naval Institute's Pilot-Follow methodology allowed for the formation flight of 20 fixed-wing UAVs in the fixed-wing sector [21]. Fixed-wing drone clusters are mostly military research experiments in confrontation, pursuit, attack, and other scenarios since they are more frequently deployed for military purposes. The 'Pixie' project of the Defense Advanced Research Projects Agency (DARPA) attempts to launch, fly, and land a number of X-61A drones from the air. The Partridge project is comparable. A large-scale fixed-wing UAV cluster flight project is also being worked on by China Electronics Technology Group (CETG), but only a small number of videos ranging from 67 to 200 have been made public. The majority of comparable military projects have the specifics of their work kept secret. The enemy mission must create its own cluster advantage for the UAV cluster to prevail in a conflict, such as the number of advantages, coordination in intelligence advantage, firepower advantage, etc. As a result, intelligent combat is a key area for multi-UAS development, and it consists of four components, of which the UAV cluster is one: the combat model framework, effectiveness analysis, cluster autonomous control decision-making methods, and experimental simulation platform [22; 23; 24; 25]. It is the primary component that controls how the multi-UAS operates. Iterative evolutionary algorithm models affected by the development of artificial intelligence, UAV swarm models [26; 27], and bionic swarm models based on behavioral norms inspired by biological swarms can all be used to govern UAV swarms autonomously [28]. The collaborative process is broken down and modeled before the sub-problems are resolved using a number of techniques [29]. The objective of the bionic swarm model is to revisit the cooperative group behavior mechanism of biological swarms and abstractly extrapolate it into the self-organizing behavior regulations of UAV swarms. Because multi-UAV swarms and bird swarms are similar in nature, different flock models of birds are frequently used in UAV swarm systems. For instance, the Beynolds-proposed BIRD model [30], which simulates bird flock behavior and exhibits the consistency of a flock of birds, is based on three fundamental rules: separation, aggregation, and coherence. The drones' perceptive capabilities and behavior norms are set up by [PERSON]'s team using a non-central decision-making control mechanism [31], which also enables distributed drone cluster search for tracking and striking targets. The [PERSON] team develops a probabilistic state transition model for autonomous behavior decision-making at the macro level of individual UAVs using the Probabilistic Finite State Machine (PFSM) method based on a decentralized hybrid architecture for multi-UAV systems to adapt to dynamic environments [32]. The team of UAV swarms developed a synchronous control approach to execute multi-point attacks on chosen targets, which showcases the swarm intelligence of UAV swarms [33]. The model's simulation results demonstrate durability and reliability, this tactic demonstrates the concept of scalability. Pheromone algorithms, artificial immune algorithms, particle swarm algorithms, simulated annealing algorithms, genetic algorithms, and other creative algorithms are used to iteratively improve the model of the UAV cluster [34; 35; 36; 37]. The cluster architecture is carefully optimized based on artificial intelligence.