TY - GEN
T1 - A novel improved teaching-learning based optimization for functional optimization
AU - Qu, Xinghua
AU - Liu, Bo
AU - Li, Zhengyang
AU - Duan, Wenzhe
AU - Zhang, Ran
AU - Zhang, Wei
AU - Li, Huifeng
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/7
Y1 - 2016/7/7
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84979774693
U2 - 10.1109/ICCA.2016.7505399
DO - 10.1109/ICCA.2016.7505399
M3 - 会议稿件
AN - SCOPUS:84979774693
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 939
EP - 943
BT - 12th IEEE International Conference on Control and Automation, ICCA 2016
PB - IEEE Computer Society
T2 - 12th IEEE International Conference on Control and Automation, ICCA 2016
Y2 - 1 June 2016 through 3 June 2016
ER -