Half-Life Teaching Factor Based TLBO Algorithm

TLDR: The proposed strategy is known as half-life teaching factor based TLBO (HLTLBO) algorithm, which is compared with various state-of-art algorithms namely, TLBOA, global-Best inspired biogeography-based optimization (GBBO), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMA-ES).

Abstract: Teaching-learning-based optimization algorithm (TLBOA) is a significant metaheuristic algorithm. It is a proficient approach for solving multidimensional, linear, and nonlinear optimization problems. It is based on teaching-learning (TL) process that searches for a global optimum through two modules of learning: (a) teacher-phase (TP) and (b) learner-phase (LP). For avoiding the premature convergence of TLBOA, half-life teaching factor is discovered in this paper. The proposed strategy is known as half-life teaching factor based TLBO (HLTLBO) algorithm. The performance of HLTLBO is calculated over 20 benchmark functions and compared with various state-of-art algorithms namely, TLBOA, global-Best inspired biogeography-based optimization (GBBO), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMA-ES). The obtained outcomes validate the authenticity of the discovered HLTLBO.