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.
ASJC Scopus subject areas
- Computer Science(all)