A Multi-Objective TLBO Algorithm with Baldwin Learning Idea for Application of SiC Cutting Process Optimization

The currently Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) only can obtain a well-distributed solution set on several bi-objective problems. To tri-objective or more problems, however, it is quite difficult to achieve optimal solutions owing to a lack of the guidance mechanism from the past and current Pareto Solution (PS) and effective measure of crowding degree. This paper presents a new MOTLBO combination Baldwin learning idea and K-nearest neighbor measure (KBTLBO) to deal with multi-objective optimization problems (MOPs), especially the SiC cutting process optimization and the promising results show the applicability of the QETLBO algorithm for problem solving.