Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning
This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-A...
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| Outros Autores: | , |
| Formato: | bookPart |
| Idioma: | eng |
| Publicado em: |
2014
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.22/4291 |
| País: | Portugal |
| Oai: | oai:recipp.ipp.pt:10400.22/4291 |
| Resumo: | This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module 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. Finally, some conclusions are reached and future work outlined. |
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