435 citations · 549 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
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…
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 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…