Book ChapterDOI
A MAX-MIN Ant System for the University Course Timetabling Problem
TLDR
The results demonstrate that the MAX-MIN Ant System is able to construct significantly better timetables than an algorithm that iterates the local search procedure from random starting solutions.Abstract:
We consider a simplification of a typical university course timetabling problem involving three types of hard and three types of soft constraints. A MAX-MIN Ant System, which makes use of a separate local search routine, is proposed for tackling this problem. We devise an appropriate construction graph and pheromone matrix representation after considering alternatives. The resulting algorithm is tested over a set of eleven instances from three classes of the problem. The results demonstrate that the ant system is able to construct significantly better timetables than an algorithm that iterates the local search procedure from random starting solutions.read more
Citations
More filters
Book ChapterDOI
Ant Colony Optimization: Overview and Recent Advances
Marco Dorigo,Thomas Stützle +1 more
TL;DR: This chapter reviews developments in ACO and gives an overview of recent research trends, including the development of high-performing algorithmic variants and theoretical understanding of properties of ACO algorithms.
Journal ArticleDOI
A Tabu-Search Hyperheuristic for Timetabling and Rostering
TL;DR: It is demonstrated that this tabu-search hyperheuristic is an easily re-usable method which can produce solutions of at least acceptable quality across a variety of problems and instances and is fundamentally more general than state-of-the-art problem-specific techniques.
Journal ArticleDOI
A Graph-Based Hyper-Heuristic for Educational Timetabling Problems
TL;DR: In this article, a generic hyper-heuristic approach based on a set of widely used graph coloring heuristics is proposed for timetabling problems, where a Tabu Search approach is employed to search for permutations of graph heuristic which are used for constructing timetables.
Journal ArticleDOI
Classification With Ant Colony Optimization
TL;DR: This paper provides an overview of previous ant-based approaches to the classification task and compares them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study, and proposes a new AntMiner+.
Journal ArticleDOI
A survey of metaheuristic-based techniques for University Timetabling problems
TL;DR: An overview of metaheuristics in university timetabling applications is carried out, paying particular attention to the various methods that have been proposed for dealing and differentiating between constraints of varying importance.
References
More filters
Numerical recipes in C
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Book
Practical Nonparametric Statistics
TL;DR: Probability Theory. Statistical Inference. Contingency Tables. Appendix Tables. Answers to Odd-Numbered Exercises and Answers to Answers to Answer Questions as discussed by the authors.
BookDOI
Swarm intelligence: from natural to artificial systems
TL;DR: This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning.
Journal ArticleDOI
Ant algorithms for discrete optimization
TL;DR: An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.