A Novel Teaching-Learning-Based Optimization with Laplace Distribution and Experience Exchange

Abstract

Teaching-Learning-Based Optimization (TLBO) algorithm is an evolutionary powerful algorithm that has better global searching capability. However, in the later period of evolution of the TLBO algorithm, the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions, which lead to a trap in local optima and premature convergence. This paper presents an improved version of the TLBO algorithm based on Laplace distribution and Experience exchange strategy (LETLBO). It uses Laplace distribution to expand exploration space. A new experience exchange strategy is applied to make good use of experience information to identify more promising solutions to make the algorithm converge faster. The experimental performances verify that the LETLBO algorithm enhances the solution accuracy and quality compared to original TLBO and various versions of TLBO and is very competitive with respect to other very popular and powerful evolutionary algorithms. Finally, the LETLBO algorithm is also applied to parameter estimation of chaotic systems, and the promising results show the applicability of the LETLBO algorithm for problem-solving.