Population based Local Search for university course timetabling problems

Anmar Abuhamdah, Masri Ayob, Graham Kendall, Nasser R. Sabar

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.

Original languageEnglish
Pages (from-to)44-53
Number of pages10
JournalApplied Intelligence
Volume40
Issue number1
DOIs
Publication statusPublished - Jan 2014

Keywords

  • Course timetabling problem
  • Gravitational emulation
  • Hybrid methods
  • Metaheuristics
  • Population based algorithm

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Population based Local Search for university course timetabling problems. / Abuhamdah, Anmar; Ayob, Masri; Kendall, Graham; Sabar, Nasser R.

In: Applied Intelligence, Vol. 40, No. 1, 01.2014, p. 44-53.

Research output: Contribution to journalArticle

Abuhamdah, Anmar ; Ayob, Masri ; Kendall, Graham ; Sabar, Nasser R. / Population based Local Search for university course timetabling problems. In: Applied Intelligence. 2014 ; Vol. 40, No. 1. pp. 44-53.
@article{17d3e2f7aec645d191526e26ffeda7ec,
title = "Population based Local Search for university course timetabling problems",
abstract = "Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.",
keywords = "Course timetabling problem, Gravitational emulation, Hybrid methods, Metaheuristics, Population based algorithm",
author = "Anmar Abuhamdah and Masri Ayob and Graham Kendall and Sabar, {Nasser R.}",
year = "2014",
month = "1",
doi = "10.1007/s10489-013-0444-6",
language = "English",
volume = "40",
pages = "44--53",
journal = "Applied Intelligence",
issn = "0924-669X",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Population based Local Search for university course timetabling problems

AU - Abuhamdah, Anmar

AU - Ayob, Masri

AU - Kendall, Graham

AU - Sabar, Nasser R.

PY - 2014/1

Y1 - 2014/1

N2 - Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.

AB - Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.

KW - Course timetabling problem

KW - Gravitational emulation

KW - Hybrid methods

KW - Metaheuristics

KW - Population based algorithm

UR - http://www.scopus.com/inward/record.url?scp=84894902078&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84894902078&partnerID=8YFLogxK

U2 - 10.1007/s10489-013-0444-6

DO - 10.1007/s10489-013-0444-6

M3 - Article

VL - 40

SP - 44

EP - 53

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

IS - 1

ER -