Best-Worst Ant Colony System Parameter Investigation by Using Experimental Design and Analysis for Course Timetabling Problem

Abstract

Course timetabling usually arises every academic year and is solved by academic staff with/without course timetabling tool. The desirable timetable must be satisfied by hard constraints whilst soft constraints are not absolutely essential. Course timetabling is known to be NP-hard problem, which means that the computational time required to find the solution increases exponentially with problem size. Automated timetabling system has been developed for university courses scheduling. In this work, new variant of Ant Colony Optimisation called Best-Worst Ant Colony System (BWACS) was applied to solve university course timetabling problem. Advance statistical tools for experimental design and analysis were used to investigate and analyse the factor influence of this system and conclude the appropriate parameter setting of BWACS.