# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence
#### 3.1.4 Bee Algorithm  
The bee algorithm is inspired by observing how honey bees breed, mate, and forage. Their behaviours form the basis of bee optimisation algorithms [43; 44]. The Honey Bee Optimization (MBO) is among the main algorithms based on the breeding and mating activities associated with honey bees and depends on swarm intelligence [45; 46]. There are several algorithms based on the Honey Bee Optimization (MBO), including Fast Marriage in Honey Bee Optimization (FMIBO), Honey Bees Mating Optimization (HBMO), and HoneyBee Optimization (HBO) [47; 48]. As mentioned earlier, the algorithm uses evolutionary behaviours, such as the random explorative behaviour of the bees, to solve problems or achieve set objectives. The principal functionality of the bee algorithm commences from a single source, typically referred to as the queen, and flows to other bees (or robots) in the colony (swarm) [49]. The principle is based on the forward pass concept, which implies that information is sent from a single source as it flows to other colony members [50]. Like the queen, a single robot in a swarm is used as the source of information to guide other robots as they move in the area [51]. The algorithm's objective is to allow swarm robots to be attracted to a goal with the highest solution when deployed to survey a large expanse [52]. Ref. [53] proposed a novel concept of surveillance robotics that is based on honey bees and with the integration of an autonomous telepresence robot. The telepresence robot means considering human control in the loop, as doing so is essential to improve robotic swarm efficiency and speed up convergence. Experiments used Turtlebot robots for performing a foraging task, beginning at the hive location and randomly exploring in a specific area for a particular food location. The experiments assessing a proposed swarm coordination system in an unknown environment were performed via a simulation and on real robots. The simulation was performed for three settings: 5 \(\times\) 5 m\({}^{2}\) shaped, 10 \(\times\) 10 m\({}^{2}\) shaped, and 10 \(\times\) 10 m\({}^{2}\) L-shaped. When Turtlebots find a food source, they take it and drive it to the hive, where they put food until they run out of food or detect another source. The telepresence represents a leader who sends related information about the location to the Turtlebots. The findings show the telepresence robot's role in increasing the operation's efficiency, chiefly in dynamic and complex scenarios where the sources of food change over time. Ref. [54] focused on complex area coverage problems with special task areas such as forbidden regions or threat regions. At the beginning of the work, the adjusted area of the task was specified, and the grid was discretisation. Then, it was inspired by the labour division phenomenon of typical biological sets, such as colonies for bees and ants. The features analysis of the performance is made of both algorithms' labour division model for the ant colony (threshold model) and labour division model for the bee colony (activation-inhibition model) from the concept of individual and environment, individual and individual, and a novel swarm intelligence labour division method to solve the problem of complex area coverage in swarm robots. Three experiments are implemented by encompassing area coverage problems for the non-threat region, established threat region, and sudden threat region. The results show the capability of the proposed algorithm to solve area coverage and the dynamic environment. The algorithm can respond effectively to the sudden threat. Ref. [55] referred to a set of autonomous cyber-physical robotic cleaners that have been controllers for wet-cleaning rooms in large public or commercial buildings. It introduces different control strategies for the movements of a robot set or those of robots within a set and the influential factors on the intragroup behaviour of swarm robots. It presented an approach to building formations for robots depending on the leading robot by the wet trails.