Optimizing Industrial Robot Performance Through Hybrid Ensemble Meta-Learning: A Multi-Objective Comparative Analysis
Abstract
Growing requirements of industrial robotics to optimize a variety of performance indicators such as energy efficiency, precision, speed, and reliability. The study introduces a new system called Hybrid Ensemble MetaLearning (HEML) which combines various machine learning algorithms with advanced meta-learning techniques in order to streamline robot performance in conflicting goals. A thorough comparative analysis of diverse ensemble strategies such as Voting, Stacking, Blending, Bagging, Boosting and Advanced Meta-Learning methods used in the manufacturing sector is made on industrial manufacturing systems. The results of our experiment prove that the suggested HEML framework shows better results in terms of accuracy (94.2 percent) when applied to solve the multi-objective optimization tasks, being much better than the traditional single-model counterparts. The Stacking ensemble approach turned out to be the most efficient approach, with its accuracy being 92.8 % high whereas Boosting approaches demonstrated high consistency with low variance (0.02). This paper offers empirical support of the effectiveness of ensemble meta-learning in solving complex optimization problems of industrial robots as well as gives a practical understanding on how to apply the concept in an actual manufacturing setting.
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