An improved teaching–learning-based optimization with neighborhood search for applications of ANN

Abstract

Teaching–learning-based optimization (TLBO) algorithm, which simulates the teaching–learning process of the class room, is one of the recently proposed swarm intelligent (SI) algorithms. The performance of TLBO is maintained by the teaching and learning process, but when the learners cannot found a better position than the old one at some successive iteration, the population might be trapped into local optima. In this paper, an improved teaching–learning-based optimization algorithm with neighborhood search (NSTLBO) is presented. In the proposed method, a ring neighborhood topology is introduced into the original TLBO algorithm to maintain the exploration ability of the population. Different than the traditional method to utilize the global information, the mutation of each learner is now restricted within a certain neighboring area so as to fully utilize the whole space and avoid over-congestion around local optima. Moreover, a mutation operation is presented to NSTLBO during the duplicate eliminations in order to maintain the diversity of population. To verify the performance of the proposed algorithm, thirty-two benchmark functions are utilized. Finally, three application problems of artificial neural network are examined. The results in thirty-two benchmark functions and three applications of ANN indicate that the proposed algorithm has shown interesting outcomes.