Timetable Scheduling Using a Hybrid Particle Swarm Optimization with Local Search Approach

Abstract

Developing an educational institution timetable is a complex problem which requires finding a successful combination of all the parameters involved (courses, professors, students, classrooms, etc.). To address this problem we developed a prototype algorithm that is a hybrid form of the Particle Swarm Optimization (PSO) algorithm. The original PSO algorithm simulates the mode of a bird cluster movement into nature. In particular, as in this case the solution to a problem with discrete values is needed, we developed a hybrid form of this algorithm with local search, in the process of which we incorporated original methods. The main contribution of this paper is how to improve particles based on optimal Gbest (Global best) and Pbest (Particle best) values of the particles. Our work provides also a fully detailed description of the innovate solution on how to update the algorithm particles in each iteration of the optimization process (Local Search). Our algorithm achieves satisfactory results within seconds.