Abstract：To figure out the problems such as the conventional quantum-genetic algorithm is easily prone to fall into local optimum，its much iteration number and its long computing time，a new algorithm named quantum genetic algorithm based on low-discrepancy monte carlo sequences is proposed.This improved algorithm realizes the balance of exploitation and exploration of quantum genetic algorithm by utilizing the good uniformity of low-discrepancy sequences.Firstly，a new low-discrepancy sequences H_ε Q-gate which is used to update the population of quantum states is proposed.It can improve the ability of algorithm exploration quantum state and reduce the number of iterations of the algorithm.Secondly，Pareto set neighborhood search is put forward.It uses low-discrepancy sequences for neighborhood search on the current near optimal solution so that the algorithm can look for a more optimal solution.This paper proposed algorithm was validated on five complex function optimization problems.The experimental results show that the searching capability of the proposed algorithm is better than traditional quantum genetic algorithm；the quality of the solution has more than two orders of magnitude increase；this paper proposed algorithm of computing time and the number of iterations is superior to the traditional quantum genetic algorithm.So that quantum genetic algorithm is introduced in the low-discrepancy sequence to achieve the balance of exploration and exploitation is feasible.
黄 山，苏一丹，覃 华，蒙祖强. 低偏差蒙特卡罗序列的量子遗传算法[J]. 小型微型计算机系统, 2017, 38(2): 398-404.
HUANG Shan，SU Yi-dan，QIN Hua，MENG Zu-qiang. Quantum Genetic Algorithm Based on Low-discrepancy Monte Carlo Sequences. Journal of Chinese Computer Systems, 2017, 38(2): 398-404.
2017, Vol. 38 
