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Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system
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Orientador(es)
Resumo(s)
A novel agent-based approach to Meta-Heuristics
self-configuration is proposed in this work. Meta-heuristics are
examples of algorithms where parameters need to be set up as
efficient as possible in order to unsure its performance. This
paper presents a learning module for self-parameterization of
Meta-heuristics (MHs) in a Multi-Agent System (MAS) for
resolution of scheduling problems. The learning is based on
Case-based Reasoning (CBR) and two different integration
approaches are proposed. A computational study is made for
comparing the two CBR integration perspectives. In the end,
some conclusions are reached and future work outlined.
Descrição
Palavras-chave
Case-based reasoning Learning Metaheuristics Multi-agent systems Scheduling