Abstract:

Aimming at the low convergent rate of the ant colony algorithm (ACA) and the disadvantage of falling into local extremum easily, this paper proposes a method of combining quantum evolutionary algorithms with ACA, which regards two probability amplitudes of quantum bits as current location of ant. When the number of ants is the same, the proposed algorithm makes the search space double and uses the quantum gate to realize the variation operation. Compared with the traditional algorithms, it has better population diversity in the optimization process and effectively avoids the prematurity and stagnation phenomenon of ACA. This method can be used in image segmentation. The experimental results show that this method is effective to solve the slow convergence rate and easy to fall into local extremum problems of ACA, and segmentation speed and precision have been improved greatly.