一种基于机器学习的条带自动包装算法选择方法

ORiON Pub Date : 2021-02-01 DOI:10.5784/36-2-686

Rosephine G. Rakotonirainy

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引用次数: 3

摘要

本文研究了基于数据挖掘技术的条形包装元启发式算法选择。给定一组已解决的条形包装问题实例，学习实例特征与算法性能之间的关系，并用于预测解决一组新的未知问题实例的最佳算法。提出了一个能够对这种关系进行建模的框架，用于自动打包算法的选择。在大量条形布局问题实例和最先进的条形布局算法的背景下，评估了所提出框架的有效性。结果表明，在给定实例中正确识别最佳算法的准确率为91%。

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A machine learning approach for automated strip packing algorithm selection

This paper deals with strip packing metaheuristic algorithm selection using data mining techniques. Given a set of solved strip packing problem instances, the relationship between the instance characteristics and algorithm performance is learned and is used to predict the best algorithms to solve a new set of unseen problem instances. A framework capable of modelling this relationship for an automated packing algorithm selection is proposed. The eﬀectiveness of the proposed framework is evaluated in the context of a large set of strip packing problem instances and the state-of-the-art strip packing algorithms. The results suggest a 91% accuracy in correctly identifying the best algorithm for a given instance.

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