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Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem

Research output: Working paper

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Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem. / Wright, M B.
Lancaster University: The Department of Management Science, 2007. (Management Science Working Paper Series).

Research output: Working paper

Harvard

Wright, MB 2007 'Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Wright, M. B. (2007). Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Wright MB. Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem. Lancaster University: The Department of Management Science. 2007. (Management Science Working Paper Series).

Author

Wright, M B. / Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem. Lancaster University : The Department of Management Science, 2007. (Management Science Working Paper Series).

Bibtex

@techreport{b9860d94478d4dffa73058bf3dde1a78,
title = "Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem",
abstract = "Iterated Local Search (ILS) is a popular metaheuristic search technique for use on combinatorial optimisation problems. As with most such techniques, there are many ways in which ILS can be implemented. The aim of this paper is to shed light on the best variants and choice of parameters when using ILS on a complex combinatorial problem with many objectives, by reporting on the results of an exhaustive set of experimental computer runs using ILS for a real-life sports scheduling problem. The results confirm the prevailing orthodoxy that a random element is ended for the ILS {"}kick{"}, but also concludes that a non-random element can be valuable if it is chosen intelligently. Under these circumstances it is also found that the best ILS acceptance criterion to choose appears to depend upon the length of the run; for short runs, a high-diversification approach works best; for very long runs a high-intensification approach is best; while between these extremes, a more sophisticated approach using simulated annealing or threshold methods appears to be best.",
keywords = "Iterated Local Search, scheduling, sports, metaheuristics, parameter choice",
author = "Wright, {M B}",
year = "2007",
language = "English",
series = "Management Science Working Paper Series",
publisher = "The Department of Management Science",
type = "WorkingPaper",
institution = "The Department of Management Science",

}

RIS

TY - UNPB

T1 - Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem

AU - Wright, M B

PY - 2007

Y1 - 2007

N2 - Iterated Local Search (ILS) is a popular metaheuristic search technique for use on combinatorial optimisation problems. As with most such techniques, there are many ways in which ILS can be implemented. The aim of this paper is to shed light on the best variants and choice of parameters when using ILS on a complex combinatorial problem with many objectives, by reporting on the results of an exhaustive set of experimental computer runs using ILS for a real-life sports scheduling problem. The results confirm the prevailing orthodoxy that a random element is ended for the ILS "kick", but also concludes that a non-random element can be valuable if it is chosen intelligently. Under these circumstances it is also found that the best ILS acceptance criterion to choose appears to depend upon the length of the run; for short runs, a high-diversification approach works best; for very long runs a high-intensification approach is best; while between these extremes, a more sophisticated approach using simulated annealing or threshold methods appears to be best.

AB - Iterated Local Search (ILS) is a popular metaheuristic search technique for use on combinatorial optimisation problems. As with most such techniques, there are many ways in which ILS can be implemented. The aim of this paper is to shed light on the best variants and choice of parameters when using ILS on a complex combinatorial problem with many objectives, by reporting on the results of an exhaustive set of experimental computer runs using ILS for a real-life sports scheduling problem. The results confirm the prevailing orthodoxy that a random element is ended for the ILS "kick", but also concludes that a non-random element can be valuable if it is chosen intelligently. Under these circumstances it is also found that the best ILS acceptance criterion to choose appears to depend upon the length of the run; for short runs, a high-diversification approach works best; for very long runs a high-intensification approach is best; while between these extremes, a more sophisticated approach using simulated annealing or threshold methods appears to be best.

KW - Iterated Local Search

KW - scheduling

KW - sports

KW - metaheuristics

KW - parameter choice

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Experiments to shed light on the best way to use Iterated Local Search for a complex combinatorial problem

PB - The Department of Management Science

CY - Lancaster University

ER -