A New Version of the Golden Eagle Optimizer Algorithm And Its Application For Solving A Trio-Objective Skillful Team Formation Problem In A Social Network

Metaheuristic methods have demonstrated their utility in tackling global optimization problems with and without constraints. However, existing state-of-the-art (SOTA) algorithms often suffer from limitations such as premature convergence, inefficient exploration-exploitation balance, and poor adaptability to complex discrete optimization problems like Team Formation (TF). The Golden Eagle Optimizer (GEO) algorithm is a promising metaheuristic that addresses some of these challenges by effectively managing its hunting spiral motion using two control parameters: cruise (exploration) and attack (exploitation). Despite its strengths, the standard GEO algorithm requires modifications to handle the discrete and multi-objective nature of the TF problem effectively. This paper proposes an amended version of GEO, called AGEO, which integrates specialized operators to enhance its performance in TF scenarios. A skillful TF aims to form teams of experts with complementary skills in social networks (SN) while optimizing multiple objectives, including minimizing communication costs, maximizing the similarity score between team members, and achieving minimal team cardinality. AGEO preserves GEO’s powerful exploitation and exploration mechanisms while introducing tailored operator strategies to overcome the challenges inherent in TF. The AGEO undergoes testing on several well-established benchmark datasets, including Universiti Malaysia Pahang (UMP), Internet Movie Database (IMDB), Association for Computing Machinery (ACM), and Database Systems & Logic Programming (DBLP). Additionally, a comparative study against SOTA metaheuristic algorithms such as Particle Swarm Optimization (PSO), Butterfly Optimization Algorithm (BOA), Crow Search Algorithm (CSA), and Jaya Algorithm demonstrates AGEO’s superior performance in forming highly optimized teams with the least communication cost, lowest team cardinality, and highest similarity score.