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中文摘要：
在量子进化计算中,量子旋转门是种群进化的主要算子,但是该算子旋转角度的选取是离散且固定的,使问题的搜索容易陷入局部最优.因此,本文提出了一种改进的量子旋转门算子,它能够自适应地计算旋转角度,使种群能够具有比较好的全局搜索能力;同时为了避免陷入局部最优,本文对旋转后的概率幅进行了修正操作.针对数据聚类问题,本文提出了一种基于改进量子旋转门的量子进化数据聚类方法.仿真对比实验表明：与采用常规的量子旋转门的算法及一些其他的进化算法相比,本文方法在聚类正确率上有了很大的改善;同时,针对具有对称分布的数据集,在统一采用对称距离测度后,本文的方法也取得了较好的效果.
英文摘要：
In traditional quantum-inspired evolutionary algorithm（QEA）,a quantum rotate gate is the main operator in a quantum population evolution.However,the choice of rotate angle is also discrete and constant,which makes the search of the problem easy to fall into local optimum.Therefore,a modified quantum rotate gate is proposed in this paper.The new gate uses adaptive method of calculation of rotation,which makes the population have a relatively good global search capability.At the same time,the probability amplitude is modified after rotation to enable population to jump out of local optimum.For data clustering problem,a quantum-inspired evolutionary algorithm based on the modified quantum rotate gate is proposed.The simulation experiment results show,compared with the algorithm based on a normal quantum rotate gate and some of other evolutional algorithms,the proposed algorithm increases correct rate of data clustering.At the same time,the simulation experiment results on the data sets of symmetrical distribution property show,compared with the algorithms adopting a symmetrical distance measure,our algorithm also achieves better results.
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