A FRAMEWORK FOR METAHEURISTIC BASED ALGORITHMS FOR TEAM FORMATION

Abstract

Effective collaboration in networks is crucial for successful team formation. The team forma tion problem involves selecting a subset of agents, referred to as a team, from a larger pool, ensuring the team meets certain desirable properties. This research focuses on selecting agents with the necessary skills, previous communication, and shared abilities, thereby minimizing communication costs. A practical application of the proposed approach is in team formation for IT projects and other team selection scenarios. In this study, we use real-world datasets, ACM,Academia Stack Exchange, DBLP and Players_20 football team dataset to evaluate our methods. Wesuggest a single-objective heuristic approach based on the Grey Wolf Optimizer (GWO) with a modified swap operation to improve upon previous team formation work. This method effectively minimizes communication costs while selecting agents with the required skills. Ex perimental results show that the Improved GWO significantly outperforms traditional methods in terms of both performance metrics and communication cost reduction. Building on this, we propose a hybrid metaheuristic approach that combines Particle Swarm Optimization (PSO) and the Jaya algorithm with a modified swap operator(PSO-Jaya). The third approach focuses on improving algorithm efficiency by integrating state space re duction techniques into the metaheuristic framework to address the increasing complexity and computational demands of the previous methods. The Employee Bee Algorithm (EBA) is en hanced with state space reduction, speeding up the computation while maintaining or improving result quality(IEB). Lastly, we consider a multi-objective optimization context for team formation. For this, we compare several metaheuristic approaches, including NSGA-II, NSGA-II with Simulated Annealing (NSGA-II-SA), NSGA with PSO (NSGA-II-PSO), and our approach Differential Evolution-based NSGA-II (NSGA-II-DE).