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Learning-Based Neural Ant Colony Optimization

Liu, Yi; Qiu, Jiang; Hart, Emma; Yu, Yilan; Gan, Zhongxue; Li, Wei


Yi Liu

Jiang Qiu

Yilan Yu

Zhongxue Gan

Wei Li


In this paper, we propose a new ant colony optimization algorithm , called learning-based neural ant colony optimization (LN-ACO), which incorporates an "intelligent ant". This intelligent ant contains a convolutional neural network pre-trained on a large set of instances which is able to predict the selection probabilities of the set of possible choices at each step of the algorithm. The intelligent ant is capable of generating a solution based on knowledge learned during training, but also guides other 'traditional' ants in improving their choices during the search. As the search progresses, the intelligent ant is also influenced by the pheromones accumulated by the colony, leading to better solutions. The key idea is that if tasks or instances share common features either in terms of their search landscape or solutions, then information learned by solving one instance can be applied to substantially accelerate the search on another. We evaluate the proposed algorithm on two public datasets and one real-world test set in the path planning domain. The results demonstrate that LN-ACO is competitive in its search capability compared to other ACO methods, with a significant improvement in convergence speed.

Presentation Conference Type Conference Paper (Published)
Conference Name GECCO 2023
Start Date Jul 15, 2023
End Date Apr 18, 2023
Acceptance Date Apr 25, 2023
Online Publication Date Jul 12, 2023
Publication Date 2023-07
Deposit Date Apr 26, 2023
Publicly Available Date Jul 12, 2023
Publisher Association for Computing Machinery (ACM)
Pages 47-55
Book Title GECCO 2023: Proceedings of the Genetic and Evolutionary Computation Conference
ISBN 9798400701191
Keywords Ant colony optimization, swarm intelligence, intelligent ant, deep learning
Publisher URL


Learning-Based Neural Ant Colony Optimization (accepted version) (1.8 Mb)

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