# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence
#### 3.1.2 Particle Swarm Optimisation (PSO)
[36] described the significance of fitness functions in the swarm robotics performance. Two maps, one for simulating an open area containing obstacles and another map with features of an apartment, were presented and used to assess a the PSO-based algorithm's performance. These maps have an area of 25 square meters, including walls and static targets. Three flexible fitness function types were used: standard (stand) and random target (rand_tar), using Euclidean distance to the nearest target and distance from the base (dist_base), which computes the Euclidean distance to the ground (launch point). It was noticed that the results of rand_tar and dist_base commands were better than the stand function. In total, 80% of the coverage area was achieved by the swarm robot after 1000 iterations through V-REP PRO EDU Platform. Ref. [37] proposed an exploration-enhanced RPSO (E2 RPSO) swarm robotic algorithm, searching for multitargets via extra exploration and extensive area coverage. Avoiding obstacles in the area enhanced the exploration capability by adjusting the dynamic Inertia Weight. Two diversities of the swarm were added, one named top-down diversity and another called the bottom-up diversity of the original PSO, which used adaptive dynamic Inertia Weight. The swarm robot detects all the targets in the covered area to handle the problem of searching for multitargets. The findings showed that this approach can balance exploration and exploitation and see more marks in a particular area without increasing time costs. Ref. [38] proposed an effective target function method by the PSO algorithm with the inverse of the perspective approach, which led to a growing speed and a decrease in the size of input information. The primary aim is to find the positions of soccer robots with their Cartesian coordinates at a precision of 5 cm without the need for a global positioningsystem (GPS) in a specified area. The playground has a dimension of 9 \(\times\) 6 m, with an extra 70 cm of margin around the ground. The implementation of the algorithm was performed through the MATLAB platform, and the simulation results showed that the number of function computations was reduced for each positioning, using the PSO algorithm. A better solution results from 50 particles (robots) randomly distributed over the whole area coverage. Ref. [39] proposed an adaptive exploration robotic PSO (AERPSO) to detect multiple static or dynamic targets quickly through cooperation between swarm robots in a particular area. The exploration was performed by avoiding local minima while exploring unexplored regions. At the same time, avoiding obstacles using evolutionary speed and the aggregation degree assists the velocity and position of the swarm robots exploring new areas to detect targets. I also used adaptive Inertia Weight to help in improving the exploration. Compared with existing methods, the simulation findings show that the AERPSO algorithm enhances search time by approximately 40% and the detection rate by 25%. It is an excellent method for multitarget searching by providing a balance between exploration and exploitation.