Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion
Authors: Jinbiao Chen, Jiahai Wang, Zhiguang Cao, Yaoxin Wu
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on classic MOCO problems show that our GIMF significantly outperforms state-of-the-art neural MOCO methods and exhibits superior generalization capability. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University, P.R. China 2Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, P.R. China 3Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R. China 4School of Computing and Information Systems, Singapore Management University, Singapore 5Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology |
| Pseudocode | No | The paper describes the methodologies and processes using prose and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available1. 1https://github.com/bill-cjb/GIMF |
| Open Datasets | Yes | The instances are generated by uniformly sampling coordinates within the range [0, 1]2M. |
| Dataset Splits | No | The paper describes using randomly selected instances for training and evaluating on 200 random instances or benchmark instances, but does not provide specific training/test/validation dataset splits (percentages or counts). |
| Hardware Specification | Yes | All methods adopt WS scalarization for fair comparisons, and are executed on a machine equipped with an RTX 3090 GPU and an Intel Xeon 4216 CPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but does not specify any software libraries or their version numbers (e.g., PyTorch, TensorFlow, Python version, CUDA version) used for the implementation. |
| Experiment Setup | Yes | Most hyperparameters for GIMF-P and GIMF-C are configured in line with the original PMOCO and CNH, respectively. For our model, L = 6, L = 3, and nb = n b = 10. The patch dimensions are fixed at w = h = 16. The model undergoes training for 200 epochs, with each epoch processing 100,000 randomly selected instances and a batch size of B = 64. The Adam optimizer (Kingma & Ba, 2015) is used with a learning rate of 10 4 (except 10 5 for Bi-KP) and weight decay of 10 6. The N weight vectors for the decomposition are generated according to Das & Dennis (1998), with N = 101 for M = 2 and N = 105 for M = 3. |