Abstract
In ant colony optimization technique (ACO), the shortest path is identified based on the pheromones deposited on the way by the traveling ants and the pheromones evaporate with the passage of time. Because of this nature, the technique only provides possible solutions from the neighboring node and cannot provide the best solution. By considering this draw back, this paper introduces a fuzzy integrated ACO technique which reduces the iteration time and also identifies the best path. The proposed technique is tested for travelling sales man problem and the performance is observed from the test results.
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George, A., Rajakumar, B.R. (2013). Fuzzy Aided Ant Colony Optimization Algorithm to Solve Optimization Problem. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_23
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DOI: https://doi.org/10.1007/978-3-642-32063-7_23
Publisher Name: Springer, Berlin, Heidelberg
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