Abstract
The ant colony algorithm (ACA) is a novel simulated evolutionary algorithm which is based on observations to behavior of some ant species. Because of the use of positive feedback mechanism, ACA has stronger robustness, better distributed computer system and easier to combine with other algorithms. However, it also has the flaws, for example mature and halting. This paper presents an optimization algorithm by used of multi-population hierarchy evolution. Each sub-population that is entrusted to different control achieves respectively a different search independently. Then, for the purpose of sharing information, the outstanding individuals are migrated regularly between the populations. The algorithm improves the parallelism and the ability of global optimization by the method. At the same time, according to the convex hull theory in geometry, the crossing point of the path is eliminated. Taking advantage of the common TSPLIB in international databases, lots of experiments are carried out. It is verified that the optimization algorithm effectively improves the convergence rate and the accuracy of reconciliation.
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Wang, X., Ni, J., Wan, W. (2010). Research on the Ant Colony Optimization Algorithm with Multi-population Hierarchy Evolution. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_28
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DOI: https://doi.org/10.1007/978-3-642-13495-1_28
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