Ant Colony Sampling with GFlowNets for Combinatorial Optimization

Minsu Kim, Sanghyeok Choi, Hyeonah Kim, Jiwoo Son, Jinkyoo Park, Yoshua Bengio
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:469-477, 2025.

Abstract

We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a multi-modal prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS’s promising performances.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-kim25a, title = {Ant Colony Sampling with GFlowNets for Combinatorial Optimization}, author = {Kim, Minsu and Choi, Sanghyeok and Kim, Hyeonah and Son, Jiwoo and Park, Jinkyoo and Bengio, Yoshua}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {469--477}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/kim25a/kim25a.pdf}, url = {https://proceedings.mlr.press/v258/kim25a.html}, abstract = {We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a multi-modal prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS’s promising performances.} }
Endnote
%0 Conference Paper %T Ant Colony Sampling with GFlowNets for Combinatorial Optimization %A Minsu Kim %A Sanghyeok Choi %A Hyeonah Kim %A Jiwoo Son %A Jinkyoo Park %A Yoshua Bengio %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-kim25a %I PMLR %P 469--477 %U https://proceedings.mlr.press/v258/kim25a.html %V 258 %X We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a multi-modal prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS’s promising performances.
APA
Kim, M., Choi, S., Kim, H., Son, J., Park, J. & Bengio, Y.. (2025). Ant Colony Sampling with GFlowNets for Combinatorial Optimization. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:469-477 Available from https://proceedings.mlr.press/v258/kim25a.html.

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