## 5 Conclusions and Future Works  
This article covered the main papers of swarm robotics strategies that were applied to target search problem by systematically answering the research questions that had been described in the literature review planning protocol. Through this meticulous process, it was possible to identify that each proposed strategy had addressed a specific constraint or restriction; hence, it became difficult to compare them directly to each other. The SR strategies emerged as a new method for dealing with target search problems in conjunction with the rise of AI, particularly in the SI field. This is supported by the increasing demand for SR field utilization in high-impact applications such as exploration for natural sources  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**SR Strategies** & **Mechanism** & **Advantages** & **Limitation** \\ \hline \multirow{6}{*}{Particle Swarm Optimization} & \multirow{6}{*}{1. Inspired from bird flocking behavior [12]. Particles (agents) flow through a problem space to search the best result (fitness) position within the search space. Particle velocity, best position, overall (global) best position will be updated during the search process & 1. Search is driven by social interaction among particles by consideration of each particles best position during the selection of the overall swarm position. & 1. Has tendency to trap in a local minimum. Does not have guaranteed convergence to a local or global minimum. \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{6}{*}{Behavior-based} & \multirow{6}{*}{1. Inspired by subsumption architecture [1]. Only one behavior can be activated at any given time and the robots’ output behavior depends on the activated behavior. The active behavior varies based on gained sensor data.} & 1. The flexibility of its input sensor combination enable the behavior-based strategy to comply with several search environment scenarios. & 1. Requires sophisticated rule comparison analysis to solve large-scale and practical problems. & Requires sophisticated rule comparison analysis to solve large-scale and practical problems. & Requires sophisticated rule comparison analysis to solve large-scale and practical problems. & Requires sophisticated rule comparison analysis to solve problems. & Requires sophisticated rule comparison analysis to solve large-scale and practical problems. or search and rescue, where the target search task is one of the important elements of the problem. During this review, it was noted that most of the SR strategies were using the SI approach, particularly the PSO method. The PSO algorithm was developed to focus on solving global optimization problems. There is a similarity between the objective of target problems and global optimization problems, i.e., searching for the best solution with minimal time-consumption. Due to this similarity, the PSO method is easily adapted to SR strategies during the solving of the target search problem. All the SR strategies that have been applied in target search problems are inspired by natural behavior; this proves that there is knowledge that can be gained from nature. From the environmental constraints' point of view, out of the 25 strategies of the selected articles, 16 strategies were implemented in empty space and 9 strategies were applied to the cluttered environment. The strategies focused on bio-inspired mechanisms, behavior-based mechanisms, random strategy mechanisms, and hybrid mechanisms. These results show that the researchers focused more on emerging swarm behaviors and interactions between the search agents than the swarm interaction between the environment during the target search problem. For further recommendations, both interactions (interaction between search agents and swarm interaction between the environment) are equally important and need to be considered.