计算机工程与应用2016，Vol.52Issue(22)：59-63,122,6.DOI:10.3778/j.issn.1002-8331.1412-0380
求解旅行商问题的改进型量子蚁群算法
Improved quantum ant colony algorithm for Traveling Salesman Problem
摘要
Abstract
Against the disadvantages of being easy to fall into local optimum and slow convergence speed on solving Traveling Salesman Problem(TSP)with traditional quantum ant colony algorithm(QACA), this paper proposes an Improved Quantum Ant Colony Algorithm(IQACA)on solving Traveling Salesman Problem. This algorithm designs a new strategy which is used to update pheromone volatilization factor adaptively, and it can update the pheromone dynamically. Further-more a new quantum revolving door is adopted to change the convergence tend of quantum probability amplitude. Three basic function extremum optimization simulation compared with traditional quantum ant colony algorithm, proves that the paper’s algorithm has better performance. Experimental results based on TSP library(TSPLIB)show that both convergence rate and optimized global results are much improved compared with other algorithms, and it can avoid falling into local optimum effectively.关键词
TSP/量子蚁群算法/改进型量子蚁群算法/量子旋转门Key words
Traveling Salesman Problem(TSP)/Quantum Ant Colony Algorithm(QACA)/Improved Quantum Ant Colony Algorithm(IQACA)/quantum revolving door分类
信息技术与安全科学引用本文复制引用
万正宜,彭玉旭..求解旅行商问题的改进型量子蚁群算法[J].计算机工程与应用,2016,52(22):59-63,122,6.基金项目
湖南省自然科学基金（No.14JJ7043）。 （）
