.png){kind=icon}
返回
Deep Reinforcement Learning for Multiobjective Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2020.2977661.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article proposes an end-to-end framework for solving multiobjective optimization problems (MOPs) using deep reinforcement learning (DRL), that we call DRL-based multiobjective optimization algorithm (DRL-MOA). The idea of decomposition is adopted to decompose the MOP into a set of scalar optimization subproblems. Then, each subproblem is modeled as a neural network. Model parameters of all the subproblems are optimized collaboratively according to a neighborhood-based parameter-transfer strategy and the DRL training algorithm. Pareto-optimal solutions can be directly obtained through the trained neural-network models. Specifically, the multiobjective traveling salesman problem (MOTSP) is solved in this article using the DRL-MOA method by modeling the subproblem as a Pointer Network. Extensive experiments have been conducted to study the DRL-MOA and various benchmark methods are compared with it. It is found that once the trained model is available, it can scale to newly encountered problems with no need for retraining the model. The solutions can be directly obtained by a simple forward calculation of the neural network; thereby, no iteration is required and the MOP can be always solved in a reasonable time. The proposed method provides a new way of solving the MOP by means of DRL. It has shown a set of new characteristics, for example, strong generalization ability and fast solving speed in comparison with the existing methods for multiobjective optimizations. The experimental results show the effectiveness and competitiveness of the proposed method in terms of model performance and running time.
Keyword:
Optimization
Training
Urban areas
Neural networks
Reinforcement learning
Traveling salesman problems
Modeling
Deep reinforcement learning (DRL)
multiobjective optimization
Pointer Network
traveling salesman problem
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Investigation of morphologies and characterization of rare earth metal samarium hexacyanoferrate and its composite with surfactant intercalated graphene oxide for sensor applications.png)
RSC Adv.
RSC Adv.IF0
PREAI
分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台OAAI
OAAI分享收藏分享收藏Injury-Dependent and Disability-Specific Lumbar Spinal Gene Regulation following Sciatic Nerve Injury in the Rat大鼠坐骨神经损伤后损伤依赖性和残疾特异性腰椎基因调控PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容