Learning-Based Neural Ant Colony Optimization
Learning-Based Neural Ant Colony Optimization
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DOI：
10.1145/3583131.3590483
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Yi Liu;Jiang Qiu;E. Hart;Yilan Yu;Zhongxue Gan;Wei Li
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文献类型：
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作者：
Yi Liu;Jiang Qiu;E. Hart;Yilan Yu;Zhongxue Gan;Wei Li
在本文中，我们提出了一种新的蚁群优化算法，称为基于学习的神经蚁群优化（LN-ACO），其中包含“智能蚂蚁”。这种智能蚂蚁包含一个在大量实例上预先训练的卷积神经网络，它能够预测算法每一步可能选择集的选择概率。智能蚂蚁能够根据训练期间学到的知识生成解决方案，但也可以指导其他“传统”蚂蚁在搜索过程中改进其选择。随着搜索的进行，聪明的蚂蚁也会受到蚁群积累的信息素的影响，从而得出更好的解决方案。关键思想是，如果任务或实例在搜索范围或解决方案方面具有共同特征，那么通过解决一个实例学到的信息可以应用于大幅加速对另一个实例的搜索。我们在路径规划领域的两个公共数据集和一个真实测试集上评估了所提出的算法。结果表明，与其他 ACO 方法相比，LN-ACO 的搜索能力具有竞争力，收敛速度显着提高。
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.