MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

Abstract

Accurate prediction of material defects from the given images will avoid the major cause in industrial applications. In this work, a Support Vector Regression (SVR) model has been developed from the given Gray Level Co-occurrence Matrix (GLCM) features extracted from Magnetic Flux Leakage (MFL) images wherein the length, depth, and width of the images are considered response values from the given features data set, and a percentage of data has been considered for testing the SVR model. Four parameters like Kernel function, solver type, and validation scheme, and its value and % of testing data that affect the SVR model’s performance are considered to select the best SVR model. Six different kernel functions, and three different kinds of solvers are considered as two validation schemes, and 10% to 30% of the testing data set of different levels of the above parameters. The prediction accuracy of the SVR model is considered by simultaneously minimizing prediction measures of both Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) and maximizing values. The Moth Flame Optimization (MFO) algorithm has been implemented to select the best SVR model and its four parameters based on the above conflict three prediction measures by converting multi-objectives into a single object using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance of the MFO algorithm is compared statistically with the Dragon Fly Optimization Algorithm (DFO) and Particle Swarm Optimization Algorithm (PSO).

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

“…•To achieve optimal performance and prevent under-or overfitting, it is essential to tweak the hyperparameters suitably utilizing techniques like as cross-validation. Decision trees (DT) [29] •Recursive partitioning is a subclass of supervised learning techniques that split the input space into smaller portions based on the input properties. •Decision trees are widely used in several domains because to their interpretability, adaptability, and fast training time.…”

“…•Decision trees are widely used in several domains because to their interpretability, adaptability, and fast training time. AdaBoostRegressor (ABR) [29] •It belongs to the ensemble learning family.•It works by combining multiple weak learners sequentially to create a strong learner. Random forest (RF) [29] •This method is classified within the category of ensemble learning techniques.…”

“…•The decision trees are created by repeatedly splitting the solution search space into minor portions. Linear regression (LR) [29] •It identifies the correlation between input and output variables. •It is widely used due to the simplicity, interpretability, and computing efficiency.…”

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

“…The MFO mimics the attraction of moths toward artificial light sources to guide the search for optimal solutions [10]. Algorithm 5 displays MFO implementation [15].…”