Abstract
In this paper we introduce an Ant Colony Optimisation (ACO) algorithm to find solutions for the well-known Knight’s Tour problem. The algorithm utilizes the implicit parallelism of ACO’s to simultaneously search for tours starting from all positions on the chessboard. We compare the new algorithm to a recently reported genetic algorithm, and to a depth-first backtracking search using Warnsdorff’s heuristic. The new algorithm is superior in terms of search bias and also in terms of the rate of finding solutions.
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Hingston, P., Kendall, G. (2004). Ant Colonies Discover Knight’s Tours. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_125
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DOI: https://doi.org/10.1007/978-3-540-30549-1_125
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