Machine Learning Techniques for Optimization in Engineering Applications
Keywords:
machine learning, engineering optimization, artificial neural networks, support vector regression, reinforcement learning.Abstract
This research investigates the application of machine learning (ML) techniques for optimizing engineering applications, focusing on four prominent algorithms: Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Reinforcement Learning (RL). Nearly every algorithm was assessed with a set of engineering optimization issues, and performance values of MSE, MAE, and R². The overall results indicated that ANN provided the best estimate of the average ice coverage as it had the lowest MSE of 0. 020 or an MAE of 0 for twelve months in the case of noncumulative normal dividends. 110, and the R-squared of the sample is equal to 0. 92. SVR continued the trend and thus had an MSE of 0.SVR came close behind the two, and it had an MSE of 0. 025, an MAE of 0. 120 and a R square of 0. 90. RL and LR also offered insights to the computations giving an MSE of 0 which was valued by the study. 030 and an R-squared of 0.The correlation between what was reported in the media and what was tweeted is the second best that can be obtained from econometric analysis and is less than perfect. 88, which includes an MSE of 0 in the LR case. 034 and an R-squared of 0. 85. Thus, the publicized study also demonstrates the capacity of the meticulous ML techniques to optimize engineering tasks notably and more accurately than references. In the further advancement of the study, more emphasis should be placed on the development of both hybrid models as well as the integration of real-time optimisation systems so that the full potential of ML can be properly utilised.
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