Fish swarm intelligent algorithm for the course timetabling problem

Abstract

In this work, a simulation of fish swarm intelligence has been applied on the course timetabling problem. The proposed algorithm simulates the movements of the fish when searching for food inside a body of water (refer as a search space). The search space is classified based on the visual scope of fishes into three categories which are crowded, not crowded and empty areas. Each fish represents a solution in the solution population. The movement direction of solutions is determined based on a Nelder-Mead simplex algorithm. Two types of local search i.e. a multi decay rate great deluge (where the decay rate is intelligently controlled by the movement direction) and a steepest descent algorithm have been applied to enhance the quality of the solution. The performance of the proposed approach has been tested on a standard course timetabling problem. Computational experiments indicate that our approach produces best known results on a number of these benchmark problems. © 2010 Springer-Verlag Berlin Heidelberg.