MPCA-ARDA for solving course timetabling problems

Abstract

This work presents a hybridization between Multi-Neighborhood Particle Collision Algorithm (MPCA) and Adaptive Randomized Descent Algorithm (ARDA) acceptance criterion to solve university course timetabling problems. The aim of this work 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 the MPCA-ARDA resembles a Hybrid Particle Collision Algorithm (HPCA) structure. The basic difference is that MPCA-ARDA hybridize MPCA and ARDA acceptance criterion, whilst HPCA, hybridize MPCA and great deluge acceptance criterion. In other words, MPCA-ARDA employ adaptive acceptance criterion, whilst HPCA, employ deterministic acceptance criterion. Therefore, MPCA-ARDA has better capability of escaping from local optima compared to HPCA and MPCA. MPCA-ARDA attempts to enhance the trial solution by exploring different neighborhood structures to overcome the limitation in HPCA and MPCA. Results tested on Socha benchmark datasets show that, MPCA-ARDA is able to produce significantly good quality solutions within a reasonable time and outperformed some other approaches in some instances.