A reformative teaching–learning-based optimization algorithm for solving numerical and engineering design optimization problems

Abstract

Teaching–learning-based optimization (TLBO) algorithm, which simulates the process of teaching–learning in the classroom, has been studied by many researchers, and a number of experiments have shown that it has great performance in solving optimization problems. However, it has an inherent origin bias in teacher phase and may fall into local optima for solving complex high-dimensional optimization problems. Therefore, an improved teaching method is proposed to eliminate the bias of converging toward the origin and enhance the ability of exploration during the convergence process. And a self-learning phase is presented to maintain the ability of exploration after convergence. Besides, a mutation phase is introduced to provide a good mixing ability among the population, preventing premature convergence. As a result, a reformative TLBO (RTLBO) algorithm with three modifications, an improved teaching method, a self-learning phase and a mutation phase, is proposed to significantly improve the performance of the TLBO algorithm. Ten unconstrained benchmark functions and three constrained engineering design problems are employed to evaluate the performance of the RTLBO algorithm. The results of the experiments show that the RTLBO algorithm is of excellent performance and better than, or at least comparable to, other available optimization algorithms in literature.