Feature selection in computer aided diagnosis is now becoming a challenging part in the classification of lung diseases. This is because, it needs to deliver results with improved accuracy and it also requires a greater number of features for analysis. The major demerit of widely utilized single-objective feature selection (FS) algorithm is that it proffers only a single optimum solution for a feature set. Here, a hybridized multi-objective particle swarm optimization with a local Tabu search (MOPSO-TS) algorithm is proposed to overcome the above demerit of the traditional single objective algorithm by producing a bag of optimum solutions which trade disparate objectives amongst themselves. The work is validated against a feature set which consists of GLCM features, shape features and GLRLM (gray-level run length matrix) extracted from lung chest tomography (CT) images. Classification is done using k-nearest neighbor with class probability and normal distribution (ND). The proposed FS method’s performance is analyzed against widely used bio-inspired FS methods such as Firefly, Particle Swarm Optimization along with Bee Colony Optimization algorithms. The numerical analysis of this model indicates that the proposed hybrid FS algorithm achieves improved performance compared to a single objective optimization algorithm in respect of specificity, accuracy, F-score, precision, sensitivity and error rate. The proposed algorithm obtains the result of 90.588% in both and specificity accuracy rate, (77.143) precision, 87.667 sensitivity rate and error rate of 0.1 which are higher on considering the other prevailing methodologies

计算机辅助诊断中的特征选择现在正成为肺部疾病分类中具有挑战性的部分。这是因为，它需要以更高的准确性提供结果，并且还需要大量的功能来进行分析。广泛使用的单目标特征选择（FS）算法的主要缺点是，它仅对特征集提供单一的最佳解决方案。在此，提出了一种带有局部禁忌搜索（MOPSO-TS）算法的混合多目标粒子群优化算法，以通过产生一包在各个目标之间进行权衡的最优解来克服传统单目标算法的上述缺点。该工作已通过包含GLCM功能的功能集进行了验证，从肺部X线断层扫描（CT）图像中提取形状特征和GLRLM（灰度级运行长度矩阵）。使用具有类概率和正态分布（ND）的k最近邻进行分类。针对广泛使用的生物启发性FS方法（如萤火虫，粒子群优化以及Bee Colony优化算法），对提出的FS方法的性能进行了分析。该模型的数值分析表明，与单目标优化算法相比，所提出的混合FS算法在特异性，准确性，F分数，精度，灵敏度和错误率方面均达到了改进的性能。该算法在特异性准确率，（77.143）精确度，87.667敏感度和错误率均为0的情况下均获得90.588％的结果。