Advance teaching–learning based optimization for global function optimization

Abstract

Teaching–Learning based optimization (TLBO) is an evolutionary powerful algorithm in optimal solutions search space that is inspired from teaching learning phenomenon of a classroom. It is a novel population based algorithm with faster convergence speed and without any algorithm specific parameters. The present work proposes an improved version of TLBO called the Advance Teaching–Learning Based Optimization (ATLBO). In this algorithm introduced a new weight parameter for more accuracy and faster convergence rate. The effectiveness of the method is compare against original TLBO on many benchmark problems with different characteristics and shows the improvement in performance of ATLBO over traditional TLBO.