Abstract
The performance of an ACO depends extremely on the cognition of each subpath, which is represented by the pheromone trails. This paper designs an experiment to explore a subpath’s exact role in the full-path generation. It gives three factors, sequential similarity ratio (SSR), iterative best similarity ratio (IBSR) and global best similarity ratio (GBSR), to evaluate some selected subpaths called r-rank subpaths in each iteration. The result shows that r-rank subpaths keep a rather stable proportion in the found best route. And then, by counting the crossed ants of a subpath in each iteration, a subpath-based pheromone modification rule is proposed to enhance the pheromone depositing strategy. It is combined with the iteration-best pheromone update rule to solve the traveling salesman problem (TSP), and experiments show that the new ACO has a good performance and robustness.
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Deng, X., Zhang, L., Feng, J. (2017). An Improved Ant Colony Optimization with Subpath-Based Pheromone Modification Strategy. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_28
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DOI: https://doi.org/10.1007/978-3-319-61824-1_28
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