A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Abstract

This paper proposes a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems to improve the shortcoming of TLBO. The proposed algorithm uses the genetic crossover and mutation strategies for improving the search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find good learner. The results are taken on six well known benchmark test functions. From results, it is observed that the proposed algorithm provides more optimized results in comparison to same class of algorithms.