Ant colonies discover knight's tours

Authors:
Philip Hingston;Graham Kendall
Affiliations:
Edith Cowan University, Australia;The University of Nottingham, UK
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 2
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Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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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.