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Self-optimization module for scheduling using case-based reasoning
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Orientador(es)
Resumo(s)
Metaheuristics performance is highly dependent of the respective parameters which need to be tuned.
Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The
process of defining which parameters setting should be used is not obvious. The values for parameters
depend mainly on the problem, the instance to be solved, the search time available to spend in solving
the problem, and the required quality of solution.
This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics,
integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems.
The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining
that systems must continuously and proactively improve their performance. For the learning
implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In
the use of Case-based Reasoning it is assumed that similar cases have similar solutions.
After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are
described. Finally, a computational study is presented where the proposed module is evaluated, obtained
results are compared with previous ones, some conclusions are reached, and some future work is referred.
It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics
and for the resolution of scheduling problems on dynamic environments.
Descrição
Palavras-chave
Autonomic computing Case-based reasoning Learning Meta-heuristics Multi-agent systems Scheduling
Contexto Educativo
Citação
Editora
Elsevier