基于Hadamard门变异的量子遗传算法
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摘要
为了解决量子遗传算法在函数优化过程中容易陷入局部极值问题,提出了一种Hadamard门变异的量子遗传算法,核心思想是利用小生境协同进化策略初始化种群,并采用动态调整量子旋转门策略对种群进行更新进化,加快算法的收敛速度,在量子变异过程中不采用量子非门变异而是利用Hadamard门变异操作,增加了种群的多样性,提高了全局搜索能力,保留了优秀信息。通过对典型复杂函数的优化测试,实验结果表明,提出的Hadamard门变异的量子遗传算法在效率和质量上与传统遗传算法和一般的量子遗传算法相比具有一定的优势。
One quantum genetic algorithm of Hadamard gate variation has been proposed to solve the problem that quantum genetic algorithm is easy to be trapped in local extremum in the function optimization process.Its core idea is to initialize the population with niche evolutionary strategies and make update evolution for the population with dynamic adjustment of quantum rotation gate strategies to speed up the convergence rate of the algorithm.In the quantum variation process,Hadamard gate variation operation has been adopted instead of quantum not gate variation,which has increased the diversity of the population,improved the global search capacity and kept excellent information.Optimization test has been made for typical complex functions.The experimental results have shown that compared with the traditional genetic algorithm and the general quantum genetic algorithm,the proposed algorithm is with certain advantages in both efficiency and quality.
One quantum genetic algorithm of Hadamard gate variation has been proposed to solve the problem that quantum genetic algorithm is easy to be trapped in local extremum in the function optimization process.Its core idea is to initialize the population with niche evolutionary strategies and make update evolution for the population with dynamic adjustment of quantum rotation gate strategies to speed up the convergence rate of the algorithm.In the quantum variation process,Hadamard gate variation operation has been adopted instead of quantum not gate variation,which has increased the diversity of the population,improved the global search capacity and kept excellent information.Optimization test has been made for typical complex functions.The experimental results have shown that compared with the traditional genetic algorithm and the general quantum genetic algorithm,the proposed algorithm is with certain advantages in both efficiency and quality.
引文
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[4]Narayanan A.An introductory tutorial to quantum computing[C].Proc of IEEE Colloquium on Quantum Computing:Theory,Applica-tions and Implications,London:IEEE Press,1997.
[5]Narayanan A,Moore M.Quantum inspired genetic algorithm[C].In:Proc of the 1996 IEEE International Conferences on Evolutionary Computation(ICE96),Nogaya,Japan,IEEE Press,1996,41-64.
[6]Han K-H.Genetic quantum algorithm and its application to combinatorial optimization problem[C].In:IEEE Proc.of the 2000Congress on Evolutionary Computation,San Diego,USA,IEEE Press,July,2000:1354-1360.
[7]王凌.量子进化算法研究发展[J].控制与决策,2008,23(12):1321-1326.Wang L.Research and development of quantum evolutionary algorithm[J].Control and Decision Making,2008,23(12):1321-1326.
[8]张葛祥,李娜,金炜东,等.一种新量子遗传算法及其应用[J].电子学报,2004,32(3):476-479.Zhang G X,Li N,Jin W D,et al.A new quantum genetic algorithm and its application[J].Chinese Journal of Electronics,2004,32(3):476-479.
[9]刘振,彭军,刘勇.小生境分布估计量子遗传算法及其仿真分析[J].计算机工程与科学,2016,(1):89-94.Liu Z,Peng J,Liu Y.A niche estimation of distribution quantum genetic algorithm and its simulation analysis[J].Computer Engineering and Science,2016,38(1):89-94.
[10]傅德胜,张蓉.一种改进的量子遗传算法研究[J].计算机仿真,2013,30(12):321-325.Fu D S,Zhang R.An improved quantum genetic algorithm[J].Computer simulation,2013,30(12):321-325.
[11]Sardana M,Agrawal R K,Kaur B.A hybrid of clustering and quantum genetic algorithm for relevant genes selection for cancer microarray data[J].International Journal of Knowledge-based and Intelligent Engineering Systems,2016,20(3):161-173.
[12]鱼佳欣,李刚,李东涛,等.改进量子遗传算法在无人机航迹规划中的应用[J].计算机仿真,2015,32(05):106-109.Yu J X,Li G,Li D T,et al.Improved quantum genetic algorithm for UAV route planning and simulation[J].Computer Simulation,2015,32(05):106-109.
[13]张宗飞.一种改进型量子遗传算法[J].计算机工程,2010,36(6):181-183.Zhang Z F.An improved quantum genetic algorithm[J].Computer Engineering,2010,36(6):181-183.