In recent years, Deep Learning based methods have been a revolution in the field of combinatorial optimization. They learn to approximate solutions and constitute an interesting choice when dealing with repetitive problems drawn from similar distributions. Most effort has been devoted to investigating neural constructive methods, while the works that propose neural models to iteratively improve a candidate solution are less frequent. In this paper, we present a Neural Improvement (NI) model for graph-based combinatorial problems that, given an instance and a candidate solution, encodes the problem information by means of edge features. Our model proposes a modification on the pairwise precedence of items to increase the quality of the solution. We demonstrate the practicality of the model by applying it as the building block of a Neural Hill Climber and other trajectory-based methods. The algorithms are used to solve the Preference Ranking Problem and results show that they outperform conventional alternatives in simulated and real-world data. Conducted experiments also reveal that the proposed model can be a milestone in the development of efficiently guided trajectory-based optimization algorithms.
翻译:近年来,深学习方法一直是组合优化领域的一场革命,它们学会了近似解决方案,并在处理类似分布的重复问题时构成了一种有趣的选择。大部分努力都用于调查神经建设性方法,而提出神经模型以迭代改进候选解决方案的工程则较少。在本文中,我们提出了一个基于图表的组合问题神经改进(NI)模型,根据实例和候选解决方案,该模型通过边缘特征对问题信息进行编码。我们的模型建议修改项目对称优先,以提高解决方案的质量。我们通过将模型用作神经山爬行器和其他轨迹法的构件来展示模型的实用性。这些算法用于解决参考排位问题,结果显示它们超越了模拟和实际世界数据中的常规替代方法。进行实验还表明,拟议的模型可以成为发展高效导轨迹优化算法的里程碑。