一种新的量子蚁群优化算法

 针对蚁群算法在求解连续空间优化问题时易于陷入局部最优和收敛速度慢的问题，提出了一种新的基于量子进化的蚁群优化算法。 该算法采用量子比特的概率幅表示蚂蚁当前位置信息；设计了一种新的量子旋转门更新蚂蚁位置, 完成蚂蚁的移动；最后采用量子 非门实现蚂蚁所在位置的变异, 增加位置的多样性。不仅从理论上证明了所提出算法的收敛性，而且通过仿真实验表明该算法可使 搜索空间加倍，比传统的蚁群算法具有更好的种群多样性，更快的收敛速度和全局寻优能力。

A Novel Quantum Ant Colony Optimizing Algorithm

Aiming at the shortcoming of optimization problems in continuous space based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate, a novel quantum ant colony optimization algorithm is presented. In this algorithm, each ant position is represented by a group of quantum bits; a new quantum rotation gates are designed to update the position of the ant so as to enable the ant to move. Some quantum bits are mutated by quantum non gate so as to increase the variety of ant positions. It not only proves the convergence of the proposed algorithms through theoretical analysis,but also demonstrates that the algorithm can double searching space,maintain better population diversity, rapider convergence speed and global optimal ability than the classical ant colony algorithm by simulation experiments.