## 4 System Components
### Computing System
#### 4.2.2 Swarm Intelligence  
The goal of swarm intelligence is to leverage the operation of drone systems to exhibit advanced and complex swarm behaviors through their cooperation, organization and information exchange. A swarm intelligence systems is made of a set of simple agents (drones in this case) interacting locally among themselves and with the environment. The operation of swarm intelligence systems aims to mimic some biological systems, where agents (e.g., ants, bees) follow simple rules which are not dictated by any central entity. In these systems the interactions among the agents give way to the emergence of global behaviors. In this context, swarm intelligence algorithms aim to control drone swarms in what concerns their behaviors based on two approaches namely optimization and consensus. This article aims to analyze optimization approaches such as PSO, ACO, and BCO [9], as well as consensus approaches mainly Paxos and Raft algorithms [79]. PSO proposed by [PERSON] and [PERSON] in 1995 is inspired by the flocking behavior of birds [9]. PSO can be used to allow drones to cooperate in searching for the best solution to solve an identified problem, such as avoiding an obstacle. For this purpose, the PSO algorithm continuously update of velocities and positions of the drones in the solution  
Figure 4: Self-Organizing Decision-Making Procedures. space. By sharing this information, the algorithm allows different drones to move towards best positions relative to their neighbors to maintain strong bonds for collective sailing in attaining optimum performance [80]. We shall present in the adjoining tables swarm intelligence algorithms implemented based on the two approaches. Table 3 references are made to optimization-based implemented algorithms, considering optimization approach, solutions provided in terms of function and deployed implemented technique. The ACO algorithm is devised to assist in finding optimal paths based on the travelling behavior of ants in search for food sources while releasing pheromones on their paths [9]. As ants pursue food sources, the release of more pheromones on a path is a likely indication of a food source. The algorithm has strong cooperative behaviors and so has strong global optimal abilities as well as flexible to implement. It is applied in combinatorial optimization problems, job scheduling problems and network optimization routing [91]. The BCO algorithm was also devised to find optimal solutions to distributed control system problems such as scheduling, clustering and engineering designs taking inspiration from the collective intelligence of bee's behavior in food search [92]. The difference towards other algorithms such as PSO and ACO is that the BCO algorithm relies on the different roles that bees have in a colony. Hence, this algorithm may have a good applicability to the optimization of swarms encompassing drones with different roles. In what concerns consensus algorithms, the goal is to allow the drone swarm to work as a logical group capable of withstanding the failures of some of its members [79]. Within the consensus algorithms, Paxos [79] remains the foundation frontier of consensus building in distributed networked devices. Paxos performs optimization process by defining peer-to-peer consensus based on single majority decision and ensuring that only one result is agreed upon. Peer nodes can suggest, lead and share equal responsi