Investigation of simulated annealing components to solve the university course timetabling problem

Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This is due to its ease of implementation and capability to escape from local optimum. This research conducts an investigation on three of SA components: the initial temperature, cooling schedule and neighborhood structure. We observed that the high initial temperature leads SA to accept any solution (wasting more computational time), whilst the lower value leads SA to quickly trap in local optimum. Based on research findings from this phase, for each component we suggested a technique to overcome the limitations. The limitations are: (i) a dynamic initial temperature mechanism that dynamically chose the suitable initial temperature for each instance problem; (ii) adaptive cooling schedule that will adjust the temperature value during the search; and (iii) a new neighborhood structure that will improve the search ability by minimizing the random selection. In the second phase. The experimental results show that the proposed techniques and approaches in all phases have outperformed the SA and comparable to other approaches in the literature (tested on university course timetabling benchmark dataset ITC2007-Track3).