Finding Feasible Timetables with Particle Swarm Optimization

A timetabling problem is usually defined as assigning a set of events to a number of rooms and timeslots such that they satisfy a number of constraints. Particle swarm optimization (PSO) is a stochastic, population-based computer problem-solving algorithm; it is a kind of swarm intelligence that is based on social-psychological principles and provides insights into social behavior, as well as contributing to engineering applications. This paper applies the particle swarm optimization algorithm to the classic timetabling problem. This is inspired by similar attempts belonging to the evolutionary paradigm in which the metaheuristic involved is tweaked to suit the grouping nature of problems such as timetabling, graph coloring or bin packing. In the case of evolutionary algorithms, this typically means substituting the "traditional operators" for newly defined ones that seek to evolve fit groups rather than fit items. We apply a similar idea to the PSO algorithm and compare the results. The results show that the number of unplaced events (error) is decreased in comparison with previous approaches.