# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 2 Related Work  
In the area of team formation studies, the effectiveness and composition of teams are critical components. Using a communication framework based on the distance diameter and a minimum spanning tree is crucial for effective team performance, as it helps in facilitating clear communication channels and optimizing team collaboration, as highlighted in [13]. In the exploration of team formation dynamics in social networks, measuring the distance between team members, whether with or without a team leader, plays a crucial role in enhancing efficiency and fostering collaborative interactions, as discussed in [14; 15]. The concept of forming expert teams where agents are interconnected through social networks has also been investigated. These studies offer valuable insights into the intricate interplay of team composition, structure, and dynamics within social networks, highlighting the importance of social connections for team effectiveness, as demonstrated by [7; 16]. These studies emphasize the critical role of strategic team formation in enhancing communication and collaboration among team members, leading to improved team performance. An efficient team, as outlined by [13], is characterized by a minimum spanning tree representing the most effective communication structure and a minimal diameter indicating the shortest path between any two team members. This study explores team dynamics by evaluating the proximity between team members in a social network, utilizing techniques with and without a team leader to cultivate effective and collaborative relationships [14; 15]. In [7; 16], the study investigates the formation of expert teams within a social network context, examining how the interconnections between agents influence their performance across various scenarios. The work [7; 16] explores the formation of expert teams when agents are connected through a social network. It explores how the interconnections between agents affect them in various situations. In [17], the authors discuss a way to have independent experts work together in spread-out groups. It uses the expert connection topology graph to find distributed maximal cliques. This approach exhibits high fault tolerance due to the experts' extensive connectivity and allows each expert to choose the most valuable coalition based on combined resources and abilities. In [18], the authors propose a genetic algorithm-based method for forming groups in collaborative learning, considering various student attributes to optimize group composition. Their findings reveal that groups formed using this approach demonstrate superior outcomes, including higher task completion rates and increased overall satisfaction among group members, surpassing the results achieved through conventional methods. An improved African Buffalo Optimization algorithm for collaborative team formation in social networks is suggested in [8]. An improved Particle Swarm Optimization technique with the New Swap Operator (I-PSONSO) for the team formation problem is suggested in [9]. An improved Java algorithm with the Modified Swap Operator (I-JMSO) for solving the team formation problem is suggested in [10]. An improved Grey Wolf Optimization algorithm (I-GWO) tailored to build teams with minimized communication costs is suggested in [19]. The abbreviations are detailed in Table 1.  
In [9], the performance of the I-PSONSO algorithm was tested on the Digital Bibliographic Library Project (DBLP) dataset. In [10], the performance of the I-JMSO algorithm was evaluated using two real-life datasets: the Stack Exchange dataset and the DBLP dataset. In [19], the performance of the I-GWO algorithm is evaluated using two real-life datasets: the Stack Exchange dataset and the ACM dataset. The following research gaps are identified based on the above. Most works have tried to make improvements to the existing algorithms by making modifications on, say, the swap operator.