A novel improved teaching-learning based optimization for functional optimization
Xinghua Qu, Bo Liu, Zhengyang Li, Wenzhe Duan, Ran Zhang, Wei Zhang, Huifeng Li · 2016
Despite the global fast coarse search capability of Teaching-Learning Based Optimization (TLBO), analysis in literature on the performance of TLBO reveals it often risks getting prematurely stuck in local optima for numerical optimization problems. In this study, Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is incorporated into the conventional TLBO to enhance its local searching performance through local search operators. The proposed TLBO-BFGS would enrich the searching modes and behaviors, and balance the global exploration and local exploitation as well. Simulation results on six well-known benchmark problems and comparisons with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard TLBO show that our proposed TLBO-BFGS can effectively enhance the searching efficiency and greatly improve the searching quality.