Preference-Based Stepping Ahead Firefly Algorithm for Solving Real-World Uncapacitated Examination Timetabling Problem

Swarm-based intelligent optimization algorithms that employ the principles of collective behavior have been gaining traction as viable solutions in optimization research. One area of optimization is the Examination Timetabling Problem (ETP), which presents a significant challenge for many Higher Education Institutes (HEIs). This study proposes a novel approach for solving the Uncapacitated Examination Timetabling Problem (UETP), where a stepping-ahead mechanism is utilized with threshold acceptance in the Firefly Algorithm (FA). The proposed method improves exploration with the use of the stepping-ahead mechanism, while threshold acceptance allows for better exploitation of the search space. Initially, a neighborhood search mechanism is employed as the discretization of FA to improve solution quality, known as Kempe Chain-based neighborhoods. The proposed method is tested on seven UETP problems, with the results showing comparative performance to the best solutions available in the literature for the Toronto exam timetabling dataset. The selection of seven problems is made with exams totaling less than 400, this allows to create a manageable yet representative benchmark. The study further extends the experiment to a real-world dataset collected from an HEI. The use of a real-world dataset allows us to see the potential of the algorithm and at the same time evaluate its performance under realistic conditions and resource constraints. The proposed stepping-ahead mechanism has the potential for use in other domains, such as robotics and engineering. Overall, this paper presents a new methodology for solving the UETP that has the potential to offer superior results when compared to existing approaches.