DEVELOPMENT OF A MODIFIED PARTICLE SWARM OPTIMIZATION BASED CULTURAL ALGORITHM FOR SOLVING UNIVERSITY TIMETABLING PROBLEM
Timetabling problems are search problems in which courses must be arranged around a set of timeslots so that some constraints are satisfied. However, slow convergence speed and high computational complexity are one of drawbacks limiting the efficiency of the existing timetabling algorithms. In this paper, a Modified Particle Swarm Optimization based Cultural Algorithm which is characterized with low computational complexity and high convergence speed was developed for solving university lecture timetabling problems. The standard Particle Swarm Optimization (PSO) algorithm was modified by introducing influence factors and acceleration component in order to improve the converge speed of the algorithm. Cultural algorithm was formulated by incorporating the Modified Particle Swarm Optimization (MPSO) into its population space. Thus, the developed Modified Particle Swarm Optimization based Cultural Algorithm could be implemented and employed for solving lecture timetabling problems in higher institutions.