Optimizing Team Formation for Welfare Activities: A Study Using Four Metaheuristic Optimization Algorithms

This study compares the effectiveness of Team Formation Problem (TFP) implementation in welfare activities using four metaheuristic optimization algorithms: Jaya (JA), Sine-Cosine (SCA), Firefly (FFA), and Particle Swarm Optimization (PSO). TFP often involves a large search space with numerous variables and constraints. These metaheuristic algorithms are efficiently exploring the search space and converge to promising solutions quickly. The purpose of this paper is to determine which algorithm performs best in assembling effective volunteer teams, taking into account factors such as skills, availability, and preferences. The study uses simulations to evaluate the algorithms’ convergence speed, solution quality, and robustness. The experimental results show that SCA and PSO perform well in team formation, with SCA outperforming PSO and completing tasks faster. However, algorithm effectiveness may vary depending to the number of volunteer skills required.