Abstract
Given classic Ant Colony Algorithm only resolves the optimization problem of discrete system, this paper proposed a Quantum Ant Colony Algorithm (QACA) based on the Bloch spherical coordinate by combining Quantum Evolutionary Algorithm and Ant Colony Algorithm. This algorithm applies Bloch spherical coordinate of Qubits to represent the current position information of ants; a new quantum revolving door is designed for updating the position to achieve to watch ants’ movement. Quantum doors help to realize the variation of ants’ positions, increase the diversity. For different optimization problems, various solution space transformational models and fitness functions are planned, so as to optimally solve the target. Furthermore, simulations of function extreme value and TSP problems were conducted, which indicted that the algorithm is feasible and effective.
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Chen, X., Xia, X., Yu, R. (2012). Quantum Ant Colony Algorithm Based on Bloch Coordinates. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_53
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DOI: https://doi.org/10.1007/978-3-642-34062-8_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34061-1
Online ISBN: 978-3-642-34062-8
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