Ant colony optimization for navigating complex labyrinths

Authors:
Zhong Yan;Chun-Wie Yuan
Affiliations:
Department of Biomedical Engineering, Southeast University, Nanjing, China;Department of Biomedical Engineering, Southeast University, Nanjing, China
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

Citing 2
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Abstract

Navigating complex labyrinths was very difficult and time-consumed. A new way to solve this problem, called Ant Colony Optimization (ACO), was proposed. The main characteristics of ACO were positive feed-back, distributed computation and the use of a constructive greedy heuristic. The object of this paper was to apply this approach to navigating complex labyrinths and finding the shortest paths for the traffic networks. To do these problems different updating rules of pheromone were tested and different versions of ACO were compared. The experiments indicated that ACO with step-by-step updating rule was more efficient than others. And the results also suggested that ACO might find wide applications in the traffic management.