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. The structure of PCA resembles a simulated annealing structure. The basic difference is that PCA does not have cooling schedule and it does not rely on user-defined parameters. PCA differs from Simulated Annealing and other meta-heuristic approaches where, before accepting the trial solution (although we obtain good-quality solution). Therefore, PCA is capable of escaping from local optima. The Hybrid Multi-Neighbourhood Particle Collision Algorithm with Great Deluge using Composite Neighbourhood Structure (HPCA), which it is hybridize the Great Deluge acceptance criterion with PCA and enhances a PCA approach that was originally introduced by Sacco for policy optimization. HPCA differs from basic PCA in terms of applying multi-neighbourhood composite structures, which is divided into two stages, one in the solution construction phase and the other in the improvement phase. HPCA also differs from basic PCA in terms of accepting the worst solution in the scattering phase, which is hybrid the Great Deluge acceptance criterion with PCA. HPCA attempts to further enhance the trial solution by exploring different neighbourhood structures. Results tested on Socha benchmark datasets show that HPCA is able to produce significantly good quality solutions within a reasonable time and outperformed some other approaches in some instances.