摘要
Combinatorial Optimization Problems(COPs)are a class of optimization problems that are commonly encountered in industrial production and everyday life.Over the last few decades,traditional algorithms,such as exact algorithms,approximate algorithms,and heuristic algorithms,have been proposed to solve COPs.However,as COPs in the real world become more complex,traditional algorithms struggle to generate optimal solutions in a limited amount of time.Since Deep Neural Networks(DNNs)are not heavily dependent on expert knowledge and are adequately flexible for generalization to various COPs,several DNN-based algorithms have been proposed in the last ten years for solving COPs.Herein,we categorize these algorithms into four classes and provide a brief overview of their applications in real-world problems.
基金
supported by the National Natural Science Foundation of China(Nos.62173258 and 61773296).
作者简介
correspondence:Feng Wang,received the BEng and PhD degrees in computer science from Wuhan University,China in 2003 and 2008,respectively.She is currently a professor at School of Computer Science,Wuhan University,China.Her research interests include evolutionary computation,intelligent information retrieval,and machine learning.She serves as a reviewer for several international journals and conferences.E-mail:fengwang@whu.edu.cn;Qi He,received the BEng degree in intelligent science and technology from Nanjing University of Science and Technology,China in 2020.She is currently a master student at Wuhan University,China.Her research interests focus on deep reinforcement learning and combinatorial optimization.heqi_049@whu.edu.cn;Shicheng Li,received the BEng degree in computer science and technology from Wuhan University,China in 2022.He is currently a master student at Wuhan University,China.His research interests focus on reinforcement learning and deep learning.lishicheng@whu.edu.cn。
