Multi-Objective Combinatorial Optimization Algorithm Based on Asynchronous Advantage Actor–Critic and Graph Transformer Networks

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 13; no. 19; p. 3842
Main Authors: Jia, Dongbao, Cao, Ming, Hu, Wenbin, Sun, Jing, Li, Hui, Wang, Yichen, Zhou, Weijie, Yin, Tiancheng, Qian, Ran
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2024
Subjects:
Algorithms
Cities
Combinatorial analysis
Decision making
Decomposition
Deep learning
Efficiency
Electric transformers
Entrapment
Genetic algorithms
Graphical representations
Heuristic
Mathematical optimization
Multiple objective analysis
Optimization
Optimization algorithms
Transformers
Traveling salesman problem
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13193842