## 1 Introduction  
In nuclear power plants, critical components such as steam generator tubes (SGT), feed water heaters, and pressure vessels have stringent design requirements due to the high temperature, pressure, and radiation-exposing environment, which induce stress corrosion cracking, pitting, fouling, and mechanical fretting [1]. Apart from that, steam generator tube rupture (SGTR) leads to the inevitable release of radiation into the environment [2]. For the safety and reliable operation of the plant, it is necessary to go for online monitoring and periodic inspection [3, 4]. Non-destructive testing (NDT) is a mode of testing that has been adopted by industries for more than a decade to test mass-manufactured products for anomalies. This process is increasingly being used by sectors such as aerospace, oil and gas, petroleum, nuclear, and construction industries [5, 6, 7]. More part failures occur due to the complexity of the devices produced. To avoid such things, NDT findings forecast failure and enhance the safety and economy of enterprises. Sumptuous non-destructive evaluation techniques have been established for major systems such as power plants and airplanes to confirm the durability of the NDT test and their safety. [PERSON] et al. [8] proposed the magnetic flux leakage (MFL) technique to identify the localized faults in the SGT. [PERSON] et al. [9] implemented the MFL technique to detect both shallow surface and deep sub-surface defects in ferromagnetic materials. [PERSON] et al. [10, 11] suggested the MFL approach for the detection of defects and subsurface cracks in small-diameter SGT. The experimental setup for the measurement of MFL is detailed in [PERSON] et al. [10]. [PERSON] et al. [12] designed an ANN model to forecast the SGT's defect in terms of the length, breadth, and depth of the crack by providing the gray-level co-occurrence matrix (GLCM) information extracted from the MFL images. [PERSON] et al. [13] studied a detailed review of the application of ML models in predicting the outcomes of stroke with structured data. The random forest (RF) ML algorithm has been implemented to predict the outcome of endovascular treatment [14] and acute stroke [15] and improve prediction accuracy in Ischemic stroke patients. Decision tree (DT) and extreme gradient boosting (XGB) ML algorithms were implemented to improve the accuracy of the prediction of Ischemic stroke patients [16]. In ML, feature selection, a method of selecting independent parameters, reduces the computation time and complexity of the problem by eliminating irrelevant, not-useful, and redundant features. Due to its combinatorial nature, feature selection is treated as a non-polynomial hard problem, which creates the opportunity for the researchers to implement meta-heuristic algorithms. [PERSON] et al. [17] proposed a new variant of the Golden Jackel Optimization (GJO) algorithm called Improved GJO (IGJO) to solve the feature selection problem in ML. [PERSON] et al. [18] introduced quadratic interpolation with a salp swarm-based local escape operator in the feature selection of 19 datasets and conducted Friedman and Wilcoxon tests to analyze the results. [PERSON] et al. [19] implemented a centroid mutation-based search and rescue optimization algorithm for feature selection in 15 disease data sets with different feature sizes extracted from the UCI machine learning repository and compared the performance with six existing meta-heuristic algorithms. Filter-based and wrapper-based techniques are the two basic categories under which feature selection may be classified [20, 21]. While the wrapper-based strategy bases its evaluation of the solution throughout the searching and optimization procedures on the learning algorithm, the filter-based approach uses the correlation between the data and the relevant class label without consulting the learning algorithm.