混合量子算法及其在flow shop问题中的应用  

Hybrid quantum algorithm and its application in flow shop problem

作　　者：傅家旗[1] 叶春明[1] 谢金华[1] 

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《计算机工程与应用》2008年第20期48-50,95,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(the National Natural Science Foundation of China under Grant No.70672110);上海市重点学科资助项目(No.T0502)

摘　　要：量子进化算法(QEA)是目前较为独特的优化算法,它的理论基础是量子计算。算法充分借鉴了量子比特的干涉性、并行性,使得QEA求解组合优化问题具备了可行性。由于在求解排序问题中,算法本身存在收敛慢,没有利用其它未成熟个体等缺陷,将微粒群算法(PSO)及进化计算思想融入QEA中,构成了混合量子算法(HQA)。采用flowshop经典问题对算法进行了测试,结果证明混合算法克服了QEA的缺陷,对于求解排序问题具有一定的普适性。Quantum Evolutionary Algorithm （QEA） is a distinctive type of algorithm for optimization currently,and the theoretical basis of QEA is quantum computation.The algorithm takes advantage of intervention and parallelism of quantum bit thoroughly, which enables QEA to solve combinatorial optimization problems.While solving scheduling problems,QEA has defects that it converges slowly and doesn＇t use other immature individual.Hybrid Quantum Algorithm （HQA） is formed and it sucks Particle Swarm Optimization algorithm （PSO） and evolutionary computation into QEA.Classical flow shop problem is employed to test the algorithm,and the result shows that the hybrid algorithm overcomes the defects of QEA and it has universality to solve scheduling problems.

关 键 词：量子进化算法 量子比特 微粒群算法 混合量子算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP301.4[自动化与计算机技术—控制科学与工程]