Abstract
Ant Colony Optimization (ACO) Algorithm is a novel stochastic search technology, which simulates the social behavior of ant colony. This paper firstly analyzes the shortcomings of basic ACO, then presents an enhanced ACO algorithm which is more faithful to real ants’ behavior in application of pheromone diffusion. By setting up the pheromone diffusion model, the algorithm improves the collaboration among the nearby ants. The simulation results show that the proposed algorithm can not only get much more optimal solutions but also greatly enhance convergence speed.
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Zhu, P., Zhao, Ms., He, Tc. (2010). A Novel Ant Colony Optimization Algorithm in Application of Pheromone Diffusion. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_1
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DOI: https://doi.org/10.1007/978-3-642-15597-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15596-3
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