多智能体调度系统中元启发式参数调整的多学徒学习

2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) Pub Date : 2012-11-01 DOI:10.1109/NaBIC.2012.6402236

I. Pereira, A. Madureira, Paulo Moura Oliveira

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引用次数: 1

摘要

在复杂性理论中，调度问题被认为是一个NP-hard组合优化问题。元启发式在解决这类问题时被证明是非常有用的。然而，这些技术需要参数调优，这是一项非常困难的任务。为了解决多智能体调度系统中的参数调整问题，提出了一种基于实例的推理模块。为了评估所提出的CBR模块的性能，进行了计算研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multi-apprentice learning for meta-heuristics parameter tuning in a Multi Agent Scheduling System

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed 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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2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)

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