Binary merge model representation of the graph colouring problem

This paper describes a novel representation and ordering model that, aided by an evolutionary algorithm, is used in solving the graph k-colouring problem. Its strength lies in reducing the number of neighbors that need to be checked for validity. An empirical comparison is made with two other algorithms on a popular selection of problem instances and on a suite of instances in the phase transition. The new representation in combination with a heuristic mutation operator shows promising result

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing feasible examination timetable while meta-heuristics are applied for improving the solution quality. This paper presents the performances of graph heuristics and major trajectory metaheuristics or S-metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristic with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 dataset respectively. Experimental results indicate that, in terms of construction of solution of datasets, hybridizing of SD produces the best initial solutions. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark examination timetabling datasets

A process for automated class scheduling at Ashesi

Applied project submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2014At the beginning of every semester, the registrar at Ashesi University goes through the laborious task of either manually or semi-automatically developing a course schedule. Very often, after the schedule has been developed, conflicts are realized in the various schedules. Conflicts are categorized into student, lecturer and room conflicts. An open source software, FET was recently used by the university to help develop schedules for the courses. This project is an attempt to review the ways in which the automation process can be enhanced in order to potentially reduce the conflicts faced. At the heart of automated course scheduling is the algorithm being used. Any effort made at enhancing the scheduling process in Ashesi will require an efficient algorithm. This paper begins with a background on scheduling, an extensive research on existing approaches and algorithms follows. The algorithms reviewed include the Multi-Agent System approach, Sequential methods, Constraint Based Methods, Genetic Algorithms, Simulated Annealing, Particle Swarm optimization and Tabu Search. The algorithm used in the FET software is also reviewed. These techniques are compared based on their computational time, ease of implementation, solution quality and constraint handling. Based on the literature, it is realized that Particle Swarm Optimization is potentially the best algorithm with respect to the set criteria. A basic version of the Particle Swarm Algorithm is implemented and tested and the results compared with the results from testing the current FET software algorithm, recursive swapping. The outcome implies that recursive swapping, can produce good solutions but Particle Swarm Optimization is easier to implement.Ashesi University CollegeGrade: less than