摘要
针对蚁群算法求解旅行商问题时易陷入局部最优和收敛速度慢的问题,提出一种新的求解旅行商问题的混合量子蚁群算法。该算法采用量子比特的概率幅对各路径上的信息素进行编码,采用量子旋转门及蚂蚁走过的路径对信息素进行更新,设计一种新的变换邻域准则。基于TSPLIB的仿真实验结果表明了该算法具有较快的收敛速度和求解精度。
Aiming at the Traveling Salesman Problem based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate, a hybrid quantum ant colony optimization algorithm is presented. In this algorithm, the pheromone on each path is encoded by a group of quantum bits, the quantum rotation gate and ant＇ s tour are used to update the pheromone so as to accelerate its convergence speed. To avoid the search falling into local optimum, the new neighborhood exchange strategy is designed to improve solution efficiency. Some cases from the TSP library（TSPLIB） are used to experiment, the results show that the algorithm has rapider convergence speed and higher accuracy than the classical ant colony algorithm.
出处
《计算机工程与应用》
CSCD
2013年第22期36-39,54,共5页
Computer Engineering and Applications
基金
安徽省教育厅自然科学研究基金资助重点项目(No.2011A006)
关键词
量子蚁群算法
变换邻域准则
旅行商问题
Quantum Ant Colony Algorithm
neighborhood exchange strategy
Traveling Salesman Problem
