Search Strategies of Population-based Metaheuristics in Timetabling

The university post-enrolment course timetabling problem is difficult to solve to optimality. Metaheuristics are usually used to tackle this problem. Metaheuristics are categorised into two classes: population-based and local search. The population-based are capable of exploring the search space (diversify strategy), whilst the local search are capable of exploiting the solution space (intensify strategy). Thus, the hybridisation of both metaheursitic classes produces an effective strategy that can complement their limitation. Therefore, this book aims to investigate the means to maintain balance between diversification and intensification of the search in an effective population-based metaheuristic. To fulfill this aim, three variants of population-based metaheuristics are introduced. These are: Elitist-Ant System, Big Bang-Big Crunch and Scatter Search. These variants are chosen due to their limited ability to provide a guided search toward elite solutions while being capable of maintaining search diversity. To evaluate their effectiveness, experiments are conducted on three groups of datasets of the post-enrolment course timetabling problem.