An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

• Published in: Computers, materials & continua materials & continua, 2022, Vol.72 (1), p.1995-2014
• Main Authors: K. Alsmadi, Mutasem, M. Jaradat, Ghaith, Alzaqebah, Malek, ALmarashdeh, Ibrahim, A. Alghamdi, Fahad, Mustafa A. Mohammad, Rami, Aldhafferi, Nahier, Alqahtani, Abdullah
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Algorithms; Computational efficiency; Computing time; Decision making; Feasibility; Heuristic methods; Particle swarm optimization; Scientific papers; Sports; Swarm intelligence; Timetables
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2022.025077

Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity, capability, and capacity. Such tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or decision-making. Swarm intelligence techniques including Particle Swarm Optimization (PSO) have proved to be effective examples. Different recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling, machine scheduling, etc. However, having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is guaranteed. This research paper seeks the enhancement of the PSO algorithm for an efficient timetabling task. This algorithm aims at generating a feasible timetable within a reasonable time. This enhanced version is a hybrid dynamic adaptive PSO algorithm that is tested on a round-robin tournament known as ITC2021 which is dedicated to sports timetabling. The competition includes several soft and hard constraints to be satisfied in order to build a feasible or sub-optimal timetable. It consists of three categories of complexities, namely early, test, and middle instances. Results showed that the proposed dynamic adaptive PSO has obtained feasible timetables for almost all of the instances. The feasibility is measured by minimizing the violation of hard constraints to zero. The performance of the dynamic adaptive PSO is evaluated by the consumed computational time to produce a solution of feasible timetable, consistency, and robustness. The dynamic adaptive PSO showed a robust and consistent performance in producing a diversity of timetables in a reasonable computational time.