Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms.

生物信息学等各个领域中的数据不断增长，这导致需要寻找一种降低数据维数的方法。基因选择问题具有大量的基因（相关，冗余或噪音），这需要一种有效的方法来帮助我们检测疾病和癌症。在此问题中，通过选择少量基因来降低计算复杂性，但是必须选择相关基因以保持较高的准确性。因此，为了找到最佳的基因子集，必须设计出一种可以研究大量可能的基因子集的有效探索方法。另外，需要使用功能强大的评估方法来评估这些基因子集的相关性。在本文中，我们提出了一种新的用于基因选择的群智能算法，称为量子蛾火焰优化算法（QMFOA），该算法基于量子计算和蛾火焰优化（MFO）算法之间的混合。QMFOA的目的是识别一个小的基因子集，该子集可用于高精度分类样品。QMFOA具有简单的两阶段方法，第一阶段是预处理，用于解决高维数据的困难，这些数据测量基因的冗余性和相关性，以获得相关的基因集。第二阶段是MFOA，量子计算和支持向量机之间的杂交，具有留一法交叉验证等功能，以解决基因选择问题。我们使用量子计算来确保在探索空间和探索空间的利用之间取得良好的折衷，而使用汉明距离和阿基米德螺旋的新更新蛾操作可以有效地探索所有可能的基因亚集。第二阶段的主要目的是确定在第一阶段获得的所有基因中最相关的基因子集。为了评估拟议的QMFOA的性能，我们在13个微阵列数据集（六个二元类和七个多类）上测试了QMFOA，以评估和比较QMFOA与许多最新发布的算法的分类准确性和选择的基因数量。实验结果表明，与其他算法相比，QMFOA具有更高的分类准确度和减少所选基因数量的能力。