# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence
#### 3.1.1 Ant Colony Optimization (ACO)  
The ACO algorithm is one of the swarm intelligence algorithms [15]. The movement of ants is influenced by their need to search for food. When ants move from one point to another in search of food, they produce organic compounds called pheromones in the form of deposits in their footpath [16; 17]. Pheromone paths enable ants to reach the food source and bring it to deposit in their colonies [18]. The ants will follow the trail with the highest quality and quantity of food [19]. Many researchers designed an ant-based algorithm applicable to swarm robots. The algorithm uses artificial intelligence robot-to-robot communication as a substitute for nature-based communication [20].  
Ref. [21] introduced the design of control laws used by swarm robots to enable them to work cooperatively. Ant colonies are used for this purpose through pheromones to control individual robot behaviour. The decentralised control law is formed using many reaction-diffusion equations, leading to area coverage. Analysis was made in this work for the effect of pheromone diffusion and evaporation on the environment and measuring the performance of area coverage. Adding noise to the control law has the greatest effect on the algorithm for covering an area, and these parameters significantly influence the performance of the area coverage. The global concept is achieved by finding the most critical parameter to be the magnitude of the noise applied. Meanwhile, the local image is also achieved by adding noise depending on the importance of the other parameters: diffusion and evaporation.  
Ref. [22] proposed a Cellular Automata Ant Memory Model (CAAM), which is used for swarm robots to perform foraging search tasks in a known environment with nests. Robots communicate with each other through the inverted pheromone. The swarm robot deposits the pheromone in each step, leading to a repulsive force among the team's individuals. In addition, to remember the robot's last positions and to avoid unnecessary explorations, I used a short-term memory inspired by Tabu Search. The forage robot has two operations, one for searching and another for homing, which is getting the food to the collection point named nest. The simulation results show that the newly proposed method can implement the foraging task competitively by covering the area effectively in searching and detecting adequate nests in homing operation.  
Ref. [23] proposed a control law for area coverage using swarm robots in a 2D region inspired by the ant foraging dynamical features. The basic idea is to increase the search efficiency of that dynamic, adaptive switching between Brownian motion and Levy flight in the stochastic component of the search. The area of 100 \(\times\) 100 square units was used for the simulations with a 10-robot swarm. The simulation results refer to enhanced performance for area coverage through swarm robots by this method in a particular threshold value.  
Ref. [24] proposed a new hybrid method that combines natural and evolutionary computing methods, using genetic algorithms, inverted ant pheromones, and Tabu Search to implement the swarm robots in surveillance tasks. The new model is named Genetic Shared Tabu Inverted Ant Cellular. An inverted pheromone indicates that when the insects are at risk, they tend to drift away from each other and increase the coverage area; in construct, when ants are in a foraging case, they tend to cluster along particular paths that lead to the food source. The simulation experiments used e-puck architecture within the Webots simulator and Python programming language.