Abstract
This paper presents an experimental analysis of how different behavior performed by a group of ants affects the optimization efficiency of the entire colony. Two different interaction ways of the ants with each other and with the environment, that is a weighted network, have been considered: (i) Low Performing Ants (LPA), which destroy nodes and links of the network making it then dynamic; and (ii) High Performing Ants (HPA), which, instead, repair the destroyed nodes or links encountered on their way. The purpose of both ant types is simply to find the exit of the network, starting from a given entrance, whilst, due to the uncertainty and dynamism of the network, the main goal of the entire colony is maximize the number of ants that reach the exit, and minimize the path cost and the resolution time. From the analysis of the experimental outcomes, it is clear that the presence of the LPAs is advantageous for the entire colony in improving its performances, and then in carrying out a better and more careful optimization of the environment.
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Notes
- 1.
Each tick correspond to an ant displacement and movement.
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Crespi, C., Scollo, R.A., Fargetta, G., Pavone, M. (2023). How a Different Ant Behavior Affects on the Performance of the Whole Colony. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_14
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