Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion
Keywords: Neural Multi-Objective Combinatorial Optimization, Multimodal Fusion, Deep Reinforcement Learning
TL;DR: This paper proposes a graph-image multimodal fusion framework for neural multi-objective combinatorial optimization.
Abstract: Existing neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integrating graph and image information of the problem instances. Our GIMF framework comprises three key components: (1) a constructed coordinate image to better represent the spatial structure of the problem instance, (2) a problem-size adaptive resolution strategy during the image construction process to improve the cross-size generalization of the model, and (3) a multimodal fusion mechanism with modality-specific bottlenecks to efficiently couple graph and image information. We demonstrate the versatility of our GIMF by implementing it with two state-of-the-art neural MOCO backbones. Experimental results on classic MOCO problems show that our GIMF significantly outperforms state-of-the-art neural MOCO methods and exhibits superior generalization capability.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 13570
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