An automated approach based on bee swarm in tackling university examination timetabling problem

Abstract

A recently invented foraging behavior optimization algorithm which is the Artificial Bee Colony (ABC) algorithm has been widely implemented in addressing various types of optimization problems such as job shop scheduling, constraint optimization problems, complex numerical optimization problems, and mathematical function problems. However, the high exploration ability of conventional ABC has caused a slowdown in its convergence speed. Inspired from the Particle Swarm Optimization (PSO) method, an automated approach has been proposed in this study and is named as the Global Best Concept - Artificial Bee Colony (GBABC) algorithm. The algorithm is formulated using the global best concept, which is then implemented into the employed bee phase to incorporate the global best solution information into solutions. This is for the sake of leading the search process towards exploring other potential search regions to locate the best global solution. In addition, to improve its exploitation ability, a local search method has been incorporated into the onlooker bee phase. With the use of the global best concept and local search method, the convergence speed, exploration and exploitation abilities of the basic ABC have been significantly enhanced. Experiments are carried out on standard university examination benchmark problems (Carter’s un-capacitated dataset). Results obtained demonstrate that, generally, the GBABC had outperformed the basic ABC algorithm in almost all instances and its performance is also comparable to other published literature.