Iterated local search using an add and delete hyper- heuristic for university course timetabling

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-cli...

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Main Authors: Soria-Alcaraz, Jorge A., Özcan, Ender, Swan, Jerry, Kendall, Graham, Carpio, Martin
Format: Article
Published: Elsevier 2016
Subjects:
Online Access:https://eprints.nottingham.ac.uk/32181/
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author Soria-Alcaraz, Jorge A.
Özcan, Ender
Swan, Jerry
Kendall, Graham
Carpio, Martin
author_facet Soria-Alcaraz, Jorge A.
Özcan, Ender
Swan, Jerry
Kendall, Graham
Carpio, Martin
author_sort Soria-Alcaraz, Jorge A.
building Nottingham Research Data Repository
collection Online Access
description Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.
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spelling nottingham-321812020-05-04T20:03:53Z https://eprints.nottingham.ac.uk/32181/ Iterated local search using an add and delete hyper- heuristic for university course timetabling Soria-Alcaraz, Jorge A. Özcan, Ender Swan, Jerry Kendall, Graham Carpio, Martin Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach. Elsevier 2016-03 Article PeerReviewed Soria-Alcaraz, Jorge A., Özcan, Ender, Swan, Jerry, Kendall, Graham and Carpio, Martin (2016) Iterated local search using an add and delete hyper- heuristic for university course timetabling. Applied Soft Computing, 40 . pp. 581-593. ISSN 1872-9681 Benchmarking; Heuristic methods; Iterative methods; Local search (optimization); Scheduling Add-delete list; Discrete optimization; Hyperheuristic; Iterated local search; Iterative framework; Optimization problems; Timetabling; University Course Timetabling Optimization http://www.sciencedirect.com/science/article/pii/S1568494615007760 doi:10.1016/j.asoc.2015.11.043 doi:10.1016/j.asoc.2015.11.043
spellingShingle Benchmarking; Heuristic methods; Iterative methods; Local search (optimization); Scheduling
Add-delete list; Discrete optimization; Hyperheuristic; Iterated local search; Iterative framework; Optimization problems; Timetabling; University Course Timetabling
Optimization
Soria-Alcaraz, Jorge A.
Özcan, Ender
Swan, Jerry
Kendall, Graham
Carpio, Martin
Iterated local search using an add and delete hyper- heuristic for university course timetabling
title Iterated local search using an add and delete hyper- heuristic for university course timetabling
title_full Iterated local search using an add and delete hyper- heuristic for university course timetabling
title_fullStr Iterated local search using an add and delete hyper- heuristic for university course timetabling
title_full_unstemmed Iterated local search using an add and delete hyper- heuristic for university course timetabling
title_short Iterated local search using an add and delete hyper- heuristic for university course timetabling
title_sort iterated local search using an add and delete hyper- heuristic for university course timetabling
topic Benchmarking; Heuristic methods; Iterative methods; Local search (optimization); Scheduling
Add-delete list; Discrete optimization; Hyperheuristic; Iterated local search; Iterative framework; Optimization problems; Timetabling; University Course Timetabling
Optimization
url https://eprints.nottingham.ac.uk/32181/
https://eprints.nottingham.ac.uk/32181/
https://eprints.nottingham.ac.uk/32181/