90 citations · 100 across the 5 of their papers we have counts for
5 papers · 1 filter
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 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…