Abstract

The formation of diverse and effective teams is crucial for organizational success, particularly in complex tasks where collaboration, communication, and innovation are essential. This study introduces a multi-objective optimization model that balances team diversity with communication efficiency while also considering the role of team familiarity in enhancing collaboration and reducing potential conflicts. By fostering mutual understanding among team members, familiarity helps mitigate challenges associated with diversity, leading to more cohesive and productive teams. Our approach utilizes advanced swarm intelligence algorithms, such as particle swarm optimization, bat algorithm, cuckoo search, grey wolf optimizer, golden eagle optimizer, and reptile swarm algorithm, to address the complex challenge of forming optimal teams. These algorithms effectively manage the complexity of multi-objective optimization, enabling a nuanced evaluation of individual skills, diversity attributes, and communication costs. The model generates teams that are both diverse and cohesive, with optimized communication dynamics. Experimental results show that cuckoo search and reptile swarm algorithms outperform others, validating the practical applicability of our approach.