Abstract
In this paper, an adaptive optimization system is established. In order to improve the global ability of basic ant colony algorithm, a novel ant colony algorithm which is based on adaptively adjusting pheromone decay parameter has been proposed, and it has been proved that for a sufficiently large number of iterations, the probability of finding the global best solution tends to 1. The simulations for TSP problem show that the improved ant colony algorithm can find better routes than basic ant colony algorithm.
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Yi, G., Jin, M., Zhou, Z. (2010). Research on a Novel Ant Colony Optimization Algorithm. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_44
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DOI: https://doi.org/10.1007/978-3-642-13278-0_44
Publisher Name: Springer, Berlin, Heidelberg
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