Experimental Result of Particle Collision Algorithm for Solving Course Timetabling Problems

This work presents a Particle Collision Algorithm (PCA) to solve university course timetabling problems. The aim is to produce an effective algorithm for assigning a set of courses, lecturers and students to a specific number of rooms and timeslots, subject to a set of constraints. PCA approach that was originally introduced by Sacco for policy optimization. PCA always accepts improved solution but adaptively accepts worse solution based on the quality of the solution. PCA differs from Simulated Annealing and other meta-heuristic approaches where, before accepting the trial solution (although we obtain good-quality solution), PCA attempts to further enhance the trial solution by exploring different neighbourhood structures. Therefore, PCA could be able of escaping from local optima. We evaluate the effectiveness of PCA. This testing it on standard test benchmark course timetabling datasets which were introduced by Socha. Results show that PCA significantly outperformed Simulated annealing (SA) and Great Deluge approach in some instances. Results also show that PCA is able to produce good quality solutions, which are comparable to other work in the literature.