An Improved Teaching-Learning-Based Optimization

Abstract

Teaching-learning-based optimization(TLBO) is a new proposed heuristic algorithm for optimization applications in recent years. In this paper, an improved TLBO algorithm (ITLBO) is presented. In the teacher phase, the second-teaching strategy and self-exploration study of teacher are introduced to improve the convergence speed. And the improved learner phase can ensure the diversity of the population to avoid the possibility of falling into a local optimum. Meanwhile, second-teaching strategy and the improved learner phase enable the algorithm to use fine local search and improve the precision. To assess the performance of ITLBO algorithm, experiments are implemented on 8 classical benchmark functions. The result show that ITLBO algorithm is an effective approach.