## 4 Result of the Systematic Literature Review
### Research Strategy Analysis
#### 4.4.1 Particle Swarm Optimization  
Particle swarm optimization, or PSO, took inspiration from birds' flocking behavior and was introduced by [PERSON] and [PERSON] [56]. Agents that are considered as particles in PSO are flow through a problem space to consider the best result (fitness) position within the search space. During the searching process, the particles update their velocity and latest best position as well as the overall best position that has been achieved within the neighborhood, either a global optimum or local optimum. For each \(i\)th iteration, the velocity (\(v\)) and position (\(x\)) will be updated as the particle performs the \(k\)th step of the PSO algorithm:  
\[v_{i}^{k}=\omega v_{i}^{k-1}+c_{1}r_{1}\left(p_{i}-x_{i}^{k-1}\right)+c_{2}r_{2 }\left(p_{n}-x_{i}^{k-1}\right) \tag{1}\]  
And:  
\[x_{i}^{k}=x_{i}^{k-1}+v_{i}^{k} \tag{2}\]  
where \(v_{i}^{k}\) and \(x_{i}^{k}\) are designated as velocity and position vectors for the \(i\)th particle in the \(k\)th time-step, \(p_{i}\) represents the personal best position of the \(i\)th particle, while \(p_{n}\) represents the overall best position of all particles within the neighborhood. \(\omega\) is the inertia weight that is introduced to balance between the global search and local search by controlling how much the current velocity of the particle contributes to its velocity in the next iteration [57]. \(c_{1}\) and \(c_{2}\) are referred to as cognitive scaling and social scaling factors, respectively, while \(r_{1}\) and \(r_{2}\) are random numbers that are drawn from a uniform distribution. The PSO algorithm was originally developed for solving global optimization problems, and there is a similarity between the objective of multiple target problems and global optimization problems, which is the searching for the best solution with minimal time-consumption. This is the main reason why the PSO method has been adapted by the researcher community when handling the target search problem using SR strategies. [PERSON] et al. in [41] reported an approach in controlling swarm autonomous wheeled mobile robots with non-holonomic constraints to search for a single target. The proposed SR strategy focused on kinematic constrain target search strategy. The proposed algorithm compared both similarities and differences with the PSO, because the proposedalgorithm was inspired by the PSO algorithm. The simulation results demonstrated that the proposed algorithm had successfully searched the target and portrayed the swarm robots to be able to cooperatively work together during the target search process. An integrated method based on a modified PSO algorithm and a grouping strategy for multiple-target search swarm robots was developed by [PERSON] et al. in [PERSON] et al. (2018). The developed algorithm has been evaluated in the simulation platform and was compared with the basic PSO algorithm to show the algorithm's effectiveness. Based on the algorithm comparison, the proposed method portrayed good adaptability and a high success rate in searching for multiple targets. In 2017, [PERSON] et al. [PERSON] et al. (2018) has continuously improved the method by incorporating the obstacle avoidance behavior into the algorithm. By considering the obstacle avoidance behavior in the algorithm, the improved grouping strategy (IGES) for multiple-target search swarm demonstrates the significant improvement in terms of adaptability, accuracy, and efficiency during the multiple target search process. Previous work by [PERSON] et al. [PERSON] et al. (2018) proposed an algorithm of swarm robots dependent solely on light energy to survive and complete target searching tasks in an unknown area. The research not only considered target searching, but also energy consumption matters. To increase the complication of the target search problem, the target and energy charging points were also scattered around the search area. For energy saving decision rules, the multi-objective particle swarm optimization (MOPSO) has been considered.