Optimizing Team Formation Using Enhanced Snake Optimizer Algorithm (ESOA): A Comparative Study with Metaheuristic Approaches
-
Abdullah Hussein
*
Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pahang,
Malaysia
- Rozlina Mohamed Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pahang, Malaysia (*) Corresponding Author
Abstract
Team formation (TF) is an important problem in various domains because it involves selecting people with complementary skills while minimizing the cost of communication. Since metaheuristic algorithms effectively handle complex optimization problems. This study proposes the Enhanced Snake Optimizer Algorithm (ESOA) to solve the TF problem and evaluates its effectiveness against the Snake Optimizer Algorithm (SOA), Jaya Algorithm (JA), and Sine–Cosine Algorithm (SCA). ESOA strengthens SOA using opposition-based learning, dynamic parameter adjustment, sine–cosine perturbation, and chaotic/Cauchy mutation to reduce premature convergence and local-optimum stagnation. Experiments were conducted on a real TF dataset from Universiti Malaysia Pahang Al-Sultan Abdullah with required-skill-set sizes of 10, 20, 30, 40, and 50, using 10 independent runs per setting. Performance was assessed using team size (TS), team cost (TC), and runtime. ESOA achieved the lowest best TC; for 50 required skills, ESOA obtained best TC = 402.641 compared with SOA = 408.026, JA = 413.854, and SCA = 425.051, while maintaining comparable TS. Although ESOA incurred higher runtime, the results indicate improved solution quality and robustness for complex TF instances, supporting practical team assembly in collaborative environments.
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