# A Survey on Swarm Robotics for Area Coverage Problem
## 4 Algorithms Analysis
 } & \multirow{4}{*}{Area coverage & \multirow{4}{*}{Unknown}} & \multirow{4}{*}{High} & \multirow{4}{*}{Moderate Static} & \multirow{4}{*}{Stochastic} & \multirow{4}{*}{Totheurist} & - Reduce computational cost & - Increasing the size of & & \\  & & & & & & - Improve the efficiency of navigation & - Increasing the size of & & \\  & & & & & - RDOS perplex path robot not to get & & the swarm above & & \\  & & & & & & & suboptimal solutions & & \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Algorithm** & **Application** & **Environment** & **Environment** & **Task** & **Obstacle** & **Type** & **Classification** & **Strength** & **Limitations** & **Ref.** & **Year** \\
**or Method** & Complex area & & & & & & & & & & & \\ coverage of & & & & & & & & & & & & \\ coverage & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\  & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline  
One notable metaheuristic algorithm with applications in surveillance jobs is the Genetic Shared Tabu Inverted Ant Cellular Automata (GSTIACA). The control settings are optimized using genetic algorithms (GAs), and it incorporates multiple AI approaches. Despite its potential, GSTIACA has not yet been implemented on actual robots, which emphasizes the importance of doing practical validation. Numerous source-seeking applications have made use of the Particle Swarm Optimization (PSO) family, which includes variations such as Inertia Weight PSO and Constriction Factor PSO. Although these algorithms are simple, resilient, and scalable, how well they perform is conditional on the surrounding conditions and the size of the swarm. With the introduction of depth-first search and a probabilistic finite-state machine, Robotic Darwinian Particle Swarm Optimization (RDPSO) demonstrates faster and more efficient navigation in exploratory tasks. Concerns with optimal time and cost arise, however, when swarm sizes are large and RDPSO becomes saturated. An essential tool for multitarget search applications, the Exploration-Enhanced RPSO (E2 RPSO) helps avoid local optima by striking a balance between the two. The techniques used here demonstrate the costs and benefits of increasing swarm size in terms of computing efficiency. By way of comparison, one traditional algorithm, D-SBPF, makes use of a grid-based environment representation, attractive and repulsive forces, and decentralized robot dispersion. Despite its simplicity and uniformity, it struggles with complex geometric maps and shows diminishing efficiency and speed with fewer robots. Although the Frontier-Led Swarming algorithm does a great job with exploratory tasks, it cannot monitor environmental changes or tune swarm settings for best performance. When combined with a Voronoi-based algorithm, Bacterial Chemotaxis Optimization (B.C.) offers benefits such as effectiveness and robustness, particularly in situations that are unknown or constantly changing. Nevertheless, there are worries about the amount of time it takes to initialize a population. The Honey Bee Algorithm has received praise for its scalability, resilience, and minimal computation, which were all influenced by the labour-division phenomenon. Still, there are situations where its sequential processing speed and evaluation of huge objective functions are a hindrance to its performance. All of the methods that were looked at show how swarm robotics has progressed to solve problems with area coverage.