Teaching-learning-based optimization with a fuzzy grouping learning strategy for global numerical optimization

Abstract

The Teaching-Learning-Based Optimization (TLBO) algorithm is a novel heuristic method that is inspired by the philosophy of teaching and learning in a class. In the Teacher Phase of the original TLBO algorithm, all learners are combined in one group and learn only from the teacher, which quickly leads to declining population diversity. Utilizing fuzzy K-means clustering to objectively divide all learners into smaller-sized groups more closely conforms to the modern idea of intra-class grouping for teaching and learning. Furthermore, fuzzy K-means clustering can objectively divide learners as nearly as possible according to their interests and abilities, which helps each learner to grow to his or her fullest extent. This paper presents a novel version of TLBO, TLBO with a Fuzzy Grouping Learning Strategy (FGTLBO), in which fuzzy K-means clustering is used to create K centers, each of which acts as the mean of its corresponding group. Performance and accuracy of the FGTLBO algorithm are examined on CEC2005 standard benchmark functions, and these results are compared with various other versions of TLBO. The experimental results verify that the FGTLBO algorithm is very competitive in terms of solution quality and convergence rate.