|Table of Contents|

Improved Quantum Crossover Based GA and Its Application to Traveling Salesman Problem(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2012年03期
Page:
43-48
Research Field:
Publishing date:

Info

Title:
Improved Quantum Crossover Based GA and Its Application to Traveling Salesman Problem
Author(s):
Yang Yu; Li Hui; Dai Hongwei
School of Computer Engineering,Huaihai Institute of Technology,Lianyungang 222005,China
Keywords:
traveling salesman problem( TSP) ; genetic algorithm ( GA) ; improved quantum crossover; optimization problem
PACS:
TP18
DOI:
-
Abstract:
In order to improve the efficiency of Genetic Algorithm ( GA) to Traveling Salesman Problem ( TSP) ，an improved quantum crossover is proposed in this paper． Compared with the traditional quantum crossover in which a city is selected according to the position，the new crossover selects a city depending on the distance comparing． The new crossover can maintain the diversity of population and generate higher quality solutions． Simulation result shows that the improved quantum crossover based GA has good ability in global exploration and local exploitation． The best solution and the average solutions on TSP are all superior to those of traditional algorithm．

References:

［1］李盼池，宋考平，杨二龙． 基于受控旋转门的量子神经网络模型算法及应用［J］． 控制与决策，2011，26( 6) : 898-901． Li Panchi，Song Kaoping，Yang Erlong． Controlled-rotating gate-based quantum neural networks model algorithm with application ［J］． Control and Decision，2011，26( 6) : 898-901． ( in Chinese)
［2］王凌，吴昊，唐芳，等． 混和量子遗传算法及其性能分析［J］． 控制与决策，2005，20( 2) : 156-160．Wang Ling，Wu Hao，Tang Fang，et al． Hybrid quantum genetic algorithms and performance analysis［J］． Control and Decision， 2005，20( 2) : 156-160． ( in Chinese)
［3］周明，孙树栋． 遗传算法原理及应用［M］． 北京: 国防工业出版社，1999． Zhou Ming，Sun Shudong． Genetic Algorithms: Theory and Applications［M］． Beijing: National Defence Industrial Press， 1999． ( in Chinese)
［4］封安辉，苏宏升． 一种改进的量子遗传算法及应用［J］． 计算机工程，2011，37( 5) : 199-201． Feng Anhui，Su Hongsheng． Improved quantum genetic algorithm and its application［J］． Computer Engineering，2011，37 ( 5) : 199-201． ( in Chinese)
［5］李英华，王宇平． 有效的混和量子遗传算法［J］． 系统工程理论与实践，2006，26( 11) : 116-124． Li Yinghua，Wan Yuping． An effective hybrid quantum genetic algorithm［J］． Systems Engineering-Theory and Practice， 2006，26( 11) : 116-124． ( in Chinese)
［6］Narayanan A，Moore M． Quantum-inspired genetic algorithms［C］/ /Proceeding of the IEEE International Conference Evolutionary Computation． Nagoya，1996: 61-66．
［7］李阳阳，焦李成． 量子克隆多播路由算法［J］． 软件学报，2007，18( 9) : 2 063-2 069． Li Yangyang，Jiao Licheng． Quantum clonal algorithm for multicast routing problem［J］． Journal of Software，2007，18( 9) : 2 063-2 069． ( in Chinese)
［8］董春龙，刘希玉． 基于遗传算法的家具造型创新设计［J］． 南京师范大学学报: 工程技术版，2010，10( 3) : 78-81． Dong Chunlong，Liu Xiyu． Creative design of furniture configuration based on genetic algorithm［J］． Journal of Nanjing Normal University: Engineering and Technology Edition，2010，10( 3) : 78-81． ( in Chinese) ［9］殷新春，杨洁． 基于遗传算法的S 盒的构造［J］． 计算机应用研究，2007，24( 3) : 91-93． Yin Xinchun，Yang Jie． Construction of S-boxes based on genetic algorithm［J］． Application Research of Computers，2007， 24( 3) : 91-93． ( in Chinese)
［10］雷秀娟，史忠科，王来军，等． 遗传算法多目标优化及其在决策支持系统中的应用［J］． 计算机应用研究，2006，23 ( 7) : 176-177． Lei Xiujuan，Shi Zhongke，Wang Laijun，et al． Multi-objective optimization of genetic algorithm and its application in DSS ［J］． Application Research of Computers，2006，23( 7) : 176-177． ( in Chinese)

Memo

Memo:
-
Last Update: 2013-03-11