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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Pereira, I., Madureira, A., De Moura Oliveira, P. B., & Abraham, A. (2013). Tuning meta-heuristics using multi-agent learning in a scheduling system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8160, pp. 190–210). Springer Verlag. https://doi.org/10.1007/978-3-642-45318-2_8
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