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

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Transactions on Computational Science XXI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 8160))

Abstract

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.

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Author information

Authors and Affiliations

  1. GECAD - Knowledge Engineering and Decision Support Group, Institute of Engineering, Polytechnic of Porto, Porto, Portugal

    Ivo Pereira & Ana Madureira

  2. INESC TEC - INESC Technology and Science, Department of Engineering, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal

    P. B. de Moura Oliveira

  3. Machine Intelligence Research Labs (MIR Labs)., Scientific Network for Innovation and Research Excellence, Auburn, USA

    Ajith Abraham

  4. IT4Innovations - Center of Excellence, VSB-Technical University of Ostrava, Czech Republic

    Ajith Abraham

Authors
  1. Ivo Pereira
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  2. Ana Madureira
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  3. P. B. de Moura Oliveira
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  4. Ajith Abraham
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Editor information

Editors and Affiliations

  1. University of Calgary, AB, Canada

    Marina L. Gavrilova

  2. CloudFabriQ Ltd., London, UK

    C. J. Kenneth Tan

  3. Technical University of Ostrava, Czech Republic

    Ajith Abraham

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Pereira, I., Madureira, A., de Moura Oliveira, P.B., Abraham, A. (2013). Tuning Meta-Heuristics Using Multi-agent Learning in a Scheduling System. In: Gavrilova, M.L., Tan, C.J.K., Abraham, A. (eds) Transactions on Computational Science XXI. Lecture Notes in Computer Science, vol 8160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45318-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-45318-2_8

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