Rank-Based Ant System via the Relative Position in a Local Hierarchy
Rank-Based Ant System via the Relative Position in a Local Hierarchy
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DOI：
10.1155/2021/8372318
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Tomoko Sakiyama;Kotaro Uneme;I. Arizono
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文献类型：
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作者：
Tomoko Sakiyama;Kotaro Uneme;I. Arizono
ASrank被提出作为蚁群优化（ACO）模型的改进版本。然而，ASrank包括在实际生物系统中不存在的行为，并落入局部解决方案中。为了解决这个问题，我们开发了ASmulti，一种新类型的ASrank，其中每个代理有助于信息素沉积估计其排名与遇到的代理。在本文中，我们试图进一步提高ASmulti的性能，允许代理考虑他们在本地层次结构中的位置。代理在所提出的模型（ASHIBERY）有助于信息素沉积估计的一致性之间的局部层次结构和全球（系统）的层次结构。我们表明，通过使用几个TSP数据集，该模型可以找到比ASmulti更好的解决方案。
ASrank has been proposed as an improved version of the ant colony optimisation (ACO) model. However, ASrank includes behaviours that do not exist in the actual biological system and fall into a local solution. To address this issue, we developed ASmulti, a new type of ASrank, in which each agent contributes to pheromone depositions by estimating its rank by interacting with the encountered agents. In this paper, we attempt further improvements in the performance of ASmulti by allowing agents to consider their position in a local hierarchy. Agents in the proposed model (AShierarchy) contribute to pheromone depositions by estimating the consistency between a local hierarchy and global (system) hierarchy. We show that, by using several TSP datasets, the proposed model can find a better solution than ASmulti.