Tuning Meta-Heuristics Using Multi-agent Learning in a Scheduling System

In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be...

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Bibliographic Details
Published inTransactions on Computational Science XXI Vol. 8160; pp. 190 - 210
Main Authors Pereira, Ivo, Madureira, Ana, de Moura Oliveira, P. B., Abraham, Ajith
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2013
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Case-based Reasoning
Learning
Metaheuristics
Parameter tuning
Scheduling
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Summary:In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be very useful in the resolution of NP-complete problems. However, these techniques require extensive parameter tuning, which is a very hard and time-consuming task to perform. Based on Multi-Agent Learning concepts, this article propose a Case-based Reasoning module in order to solve the parameter-tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.
ISBN:9783642453175
3642453171
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-45318-2_8