435 citations · 643 across the 8 of their papers we have counts for
8 papers
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks
Rui Wang, Zhiming Zhou, Tao Zhang +4
Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the…
Coevolutionary Framework for Generalized Multimodal Multi-objective Optimization
Wenhua Li, Xingyi Yao, Kaiwen Li +3
Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). Howev…
Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm
Wenhua Li, Shengjun Huang, Tao Zhang +2
Binary matrix optimization commonly arise in the real world, e.g., multi-microgrid network structure design problem (MGNSDP), which is to minimize the total length of the power sup…
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
Wei Liu, Rui Wang, Tao Zhang +3
Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades. This study seeks to solve MO…
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer
Kaiwen Li, Tao Zhang, Rui Wang +1
The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and v…
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems
Kaiwen Li, Tao Zhang, Rui Wang Yuheng Wang +1
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep…
Investigating Constraint Relationship in Evolutionary Many-Constraint Optimization
Mengjun Ming, Rui Wang, Tao Zhang
This paper contributes to the treatment of extensive constraints in evolutionary many-constraint optimization through consideration of the relationships between pair-wise constrain…
Deep Reinforcement Learning for Multi-objective Optimization
Kaiwen Li, Tao Zhang, Rui Wang
This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA. The idea of deco…