求解连续空间优化问题的量子蚁群算法

Quantum ant colony algorithm for continuous space optimization

中文关键词  量子计算  蚁群算法  连续空间优化

英文关键词  quantum computing  ant colony algorithm  continuous space optimizing

中文摘要

      针对蚁群算法只适用于离散优化问题的局限性和收敛速度慢的问题, 提出了求解连续空间优化问题的量子蚁群算法．该算法每只蚂蚁携带一组表示蚂蚁当前位置信息的量子比特; 首先根据基于信息素强度和可见度构造的选择概率, 选择蚂蚁的前进目标; 然后采用量子旋转门更新蚂蚁携带的量子比特, 完成蚂蚁的移动; 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; 最后根据移动后的位置完成蚁群信息素强度和可见度的更新．该算法将量子比特的两个概率幅都看作蚂蚁当前的位置信息, 在蚂蚁数目相同时, 可使搜索空间加倍．以函数极值问题和神经网络权值优化问题为例, 验证了算法的有效性．

英文摘要

      To tackle the shortcoming of ant colony optimization which can only be applied to discrete problems and hold a slow convergence rate, a novel method for solving optimization problems in continuous space is presented. In this algorithm, each ant carries a group of quantum bits which represent the position of the ant. Firstly, the target where the ant is going to move is selected according to the selection probability based on pheromone information and heuristic information. Secondly, quantum bits of the ant are updated by quantum rotation gates 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. Finally, pheromone information and the heuristic information are updated according to the new position of each ant arrived at. In this algorithm, both probability amplitudes of a quantum bit are regarded as position information belonging to an ant, a double searching space is acquired for ant colony which hold the same number of ants. At last, the availability of the algorithm is illustrated by two application examples of function optimization and weight optimization of neural networks.