A hybrid TLBO algorithm by quadratic approximation for function optimization and its application

Abstract

Recently hybrid optimization algorithms enjoy growing attention in the optimization community. However, over the last two decades, many new hybrid meta-heuristics optimization techniques are developed and are still developing. On the hybrid optimization algorithm, the most common criticism is that they are not well balanced in respect of the local search and global search of the algorithm. Viewing this, in the present work a modified adaptive based teaching factor is suggested for the basic TLBO algorithm. Also, a novel hybrid approach is proposed that combines the Teaching Learning Base Optimization (TLBO) Algorithm and Quadratic approximation (QA). The QA is applied to improve the global as well as local search capability of the method that also represents the characters of “Teacher Refresh”. For the performance investigation, the suggested algorithm is involved to solve twenty classical optimization functions and one real life optimization problem and the performances are differentiated with different state-of-the-arts methods in terms of numerical results of the solution.