435 citations · 549 across the 10 of their papers we have counts for
11 papers
Large Language Model Assisted Automated Algorithm Generation and Evolution via Meta-black-box optimization
Xu Yang, Rui Wang, Kaiwen Li +2
Meta-black-box optimization has been significantly advanced through the use of large language models (LLMs), yet in fancy on constrained evolutionary optimization. In this work, Aw…
Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model
Xu Yang, Rui Wang, Kaiwen Li +2
Despite significant efforts to manually design high-performance evolutionary algorithms, their adaptability remains limited due to the dynamic and ever-evolving nature of real-worl…
PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization
Xu Yang, Rui Wang, Kaiwen Li +3
The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques. Meta-Black-Box Optimization (MetaBBO), wh…
Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids
Wei Liu, Tao Zhang, Chenhui Lin +2
Independent microgrids are crucial for supplying electricity by combining distributed energy resources and loads in scenarios like isolated islands and field combat. Fast and accur…
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
Xu Yang, Rui Wang, Kaiwen Li +1
Differential evolution (DE) algorithm is recognized as one of the most effective evolutionary algorithms, demonstrating remarkable efficacy in black-box optimization due to its der…
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…
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 Orienteering Problems Based on Decomposition
Wei Liu, Tao Zhang, Rui Wang +3
This paper presents a new method for solving an orienteering problem (OP) by breaking it down into two parts: a knapsack problem (KP) and a traveling salesman problem (TSP). A KP s…
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…
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…