# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results  
For the implementation, we used Python with Anaconda Navigator's Jupyter notebook running Python 3.9 on Windows 10 with an i7 core CPU with 16 GB RAM and 512 GB SSD disk drive. The evaluations were conducted using two real-world datasets: the Academia Stack Exchange dataset and the Association for Computing Machinery (ACM) dataset. Academia Stack Exchange dataset:We used a real-life dataset, the Stack Exchange dataset, obtained from Academia Stack Exchange (June 2020), for the performance evaluation of the proposed algorithm. The dataset is structured into several key tables, which are outlined below:  
* Votes (Id, PostId, VoteTyped, CreationDate, UserId, BountyAmount)-1,048,572 records. * Users (Id, Reputation, CreationDate, DisplayName, LastAccessDate, WebsiteUrl, Location, AboutMe, Views, UpVotes, DownVotes, Age, AccountId)-127,761 records. * Tags (Id, TagName, Count, ExcerptPostId, WikiPostId)-461 records. * Posts (Id, PostTyped, AcceptedAnswerId, CreationDate, Score, ViewCount, Body, OwnerUserId, LastEditorUserId, LastEditDate, LastActivityDate, Title, Tags, AnswerCount, CommentCount, FavoriteCount)-9285 records. * PostLinks (Id, CreationDate, PostId, RelatedPostId, LinkTypedId)-20,381 records. * PostHistory (Id, PostHistoryTyped, PostId, RevisionGUID, CreationDate, UserID, Text, ContentLicense, Comment)-368,025 records. * Comments (Id, PostId, Score, Text, CreationDate, UserId, ContentLicense, UserDisplayName)-189,041 records. * Badges (Id, UserId, Name, Date, Class, TagBased)-294,449 records. Our goal was to use particular criteria to obtain an expert set and skill set from the dataset. Users with at least 10 postings make up the expert set (i.e., distinct tags) in academia.stackexchange (461 users). If there are two experts with the'share posts' tag (skills), then they become connected. The communication cost \(e_{i,j}\) of experts \(i\) and \(j\) is evaluated using Equation (1). We have considered the most important shared skills, such as 'writing,' 'writing-style', and 'peer-review' between experts extracted from the tags of distinct posts. The data were collected with the help of author collaboration, reflected in a table consisting of 51,730 records. The connections are established when one author's work is cited by another, creating a network of related data. For example, when an author shares their work with another, they are linked through shared research activities, such as graduate school involvement, entrance examinations, and the publication of scientific articles in journals, conferences, and other outlets. The dataset can be represented as 160,429 = { writing, writing-style, peer-review }, where 160,429 represents an agent, and 'writing,' 'writing-style,' and 'peer-review' are the skills associated with that agent [26]. **Association for Computing Machinery (ACM) dataset:** The ACM dataset, a widely recognized benchmark dataset for social networks, was utilized in this study to evaluate the effectiveness and validation of our proposed method. This dataset, available through the GitHub repository as of 2020, specifically focuses on author collaboration within the field of computing. We collected data on the research skills of 367 agents, representing individual researchers or contributors. The dataset is extensive, containing 3856 lines of data that detail the various skills associated with each agent. For instance, an entry in the dataset might be represented as '[EMAIL_ADDRESS] = agent computing, distributed data mining,' where '[EMAIL_ADDRESS]' identifies the agent, and the accompanying list of skills--such as agent computing, distributed data mining, and intrusion detection--represents the expertise of that agent. This structured data allowed us to accurately assess the performance of our algorithm in forming teams with low communication costs by effectively matching agents with complementary skills.