Performance improvement strategies on Cuckoo Search algorithms for solving the university course timetabling problem

The university course timetabling problem (UCTP) arises every academic year and must be solved by academic staff with/without a course timetabling tool. A Hybrid Self-adaptive Cuckoo Search-based Timetabling (HSCST) tool has been developed for minimising the total university operating costs. The HSCST tool was applied to solve eleven problem instances obtained from the Faculty of Engineering, Naresuan University. The performance improvements of the Cuckoo Search (CS) algorithm embedded within the proposed tool were demonstrated using three strategies: parameter setting approaches (static and adaptive), movement strategies (Levy flights and Gaussian random walks), and local search hybridisation techniques. Sequential computational experiments were designed and conducted to investigate the efficiency of the three proposed strategies. The statistical analysis on the computational results suggested that the proposed algorithms significantly outperformed the conventional CS, Particle Swarm Optimisation (PSO), and hybrid PSO for all problem instances. (c) 2020 Elsevier Ltd. All rights reserved.