基于数据关联感知的无监督深度融合指针网络模型

Data-correlation-aware unsupervised deep fusion pointer network model

为了提高组合优化问题可行解集合的收敛性和泛化性,根据不同无监督学习策略的特点,提出一种基于数据关联感知的深度融合指针网络模型(DMAG-PN),模型通过指针网络框架将Mogrifier LSTM、多头注意力机制与图卷积神经网络三者融合.首先,编码器模块中的嵌入层对输入序列进行编码,引入多头注意力机制获取编码矩阵中的特征信息;然后构建数据关联模型探索序列节点间的关联性,采用图卷积神经网络获取其多维度关联特征信息并融合互补,旨在生成多个嵌入有效捕捉序列深层的节点特征和边缘特征;最后,基于多头注意力机制的解码器模块以节点嵌入数据和融合图嵌入数据作为输入,生成选择下一个未访问节点的全局概率分布.采用对称旅行商问题作为测试问题,与当前先进算法进行对比,实验结果表明,所提出DMAG-PN模型在泛化性和求解精确性方面获得较大的改进与提高,预训练好的DMAG-PN模型能够直接对大规模实例进行端到端的求解,避免传统算法迭代搜索的过程,具有较高的求解效率.

In order to improve the convergence and generalization of feasible solution sets for combinatorial optimization problems,a data-correlation-aware deep fusion pointer network model(DMAG-PN)is proposed according to the characteristics of different unsupervised learning strategies.The model integrates Mogrifier LSTM,multi-head attention mechanism,and graph convolutional neural networks through a pointer network framework.Firstly,the embedding layer in the encoder module encodes the input sequence,and the multi-head attention mechanism is introduced to obtain the feature informations in the coding matrix.Secondly,the data correlation model is constructed to explore the correlation between the sequence nodes,and the graph convolution neural network is used to obtain the multi-dimensional correlation feature information,so as to generate multiple embeddings to effectively capture the deep node and edge features of the sequence.Finally,the decoder module based on multi-head attention mechanism takes node embedding data and fusion graph embedding data as inputs to generate a global probability distribution for selecting the next unvisited node.The symmetric traveling salesman problem is used as the test problem,and compared with the current advanced algorithms,the experimental results show that the proposed DMAG-PN model has been greatly improved in terms of generalization and accuracy.The pre-trained DMAG-PN model can directly solve large-scale instances,avoiding the iterative search process of traditional algorithms,and has a high solution efficiency.