Instance-specific multi-objective parameter tuning based on fuzzy logic
Instance-specific multi-objective parameter tuning based on fuzzy logic
Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new parameter tuning strategy, called IPTS, is proposed that is a novel instance-specific method to take the trade-off between solution quality and computational time into consideration. Two important steps in the method are an a priori statistical analysis to identify the factors that determine heuristic performance in both quality and time for a specific type of problem, and the transformation of these insights into a fuzzy inference system rule base which aims to return parameter values on the Pareto-front with respect to a decision maker’s preference.
Applied to the symmetric Travelling Salesman Problem and the meta-heuristic Guided Local Search, the approach is consistently faster than a traditional non-instance-specific parameter tuning strategy without significantly affecting solution quality; optimised for speed, computational times are shown to be on average 20 times faster while producing solutions of similar quality. A number of interesting areas for further research are discussed.
metaheuristics, combinatorial optimisation, travelling salesman problem, parameter setting, fuzzy logic
305-315
Ries, Jana
463ef2f9-566f-4175-b39c-b2eeb0f5b04a
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Salt, David
d627e40c-ba14-474d-b535-ed671b5f384b
16 April 2012
Ries, Jana
463ef2f9-566f-4175-b39c-b2eeb0f5b04a
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c
Salt, David
d627e40c-ba14-474d-b535-ed671b5f384b
Ries, Jana, Beullens, Patrick and Salt, David
(2012)
Instance-specific multi-objective parameter tuning based on fuzzy logic.
European Journal of Operational Research, 218 (2), .
(doi:10.1016/j.ejor.2011.10.024).
Abstract
Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new parameter tuning strategy, called IPTS, is proposed that is a novel instance-specific method to take the trade-off between solution quality and computational time into consideration. Two important steps in the method are an a priori statistical analysis to identify the factors that determine heuristic performance in both quality and time for a specific type of problem, and the transformation of these insights into a fuzzy inference system rule base which aims to return parameter values on the Pareto-front with respect to a decision maker’s preference.
Applied to the symmetric Travelling Salesman Problem and the meta-heuristic Guided Local Search, the approach is consistently faster than a traditional non-instance-specific parameter tuning strategy without significantly affecting solution quality; optimised for speed, computational times are shown to be on average 20 times faster while producing solutions of similar quality. A number of interesting areas for further research are discussed.
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e-pub ahead of print date: 4 November 2011
Published date: 16 April 2012
Keywords:
metaheuristics, combinatorial optimisation, travelling salesman problem, parameter setting, fuzzy logic
Organisations:
Operational Research
Identifiers
Local EPrints ID: 205873
URI: http://eprints.soton.ac.uk/id/eprint/205873
ISSN: 0377-2217
PURE UUID: 61775fce-75da-44ad-a157-ce84136b8c4b
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Date deposited: 14 Dec 2011 15:32
Last modified: 15 Mar 2024 03:32
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Contributors
Author:
Jana Ries
Author:
David Salt
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