|  RAO R V, SAVSANI V J, VAKHARIA D P. Teaching-learning-based optimization:a novel method for constrained mechanical design optimization problems[J]. Computer-Aided Design, 2011, 43(3):303-315.
 QU X, ZHANG R, LIU B, et al. An improved TLBO based memetic algorithm for aerodynamic shape optimization[J]. Engineering Applications of Artificial Intelligence, 2017, 57:1-15.
 朱传军,宋文家,张超勇,等.基于维修时间窗的柔性作业车间调度优化研究[J].中国机械工程,2016,27(10):1337-1343. (ZHU C J, SONG W J, ZHANG C Y, et al. Research on optimization of FJSP based on maintenance time window[J]. China Mechanical Engineering, 2016, 27(10):1337-1343.)
 GVÇYETMEZ M, ÇAM E. A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems[J]. Electrical Engineering, 2016, 98(2):145-157.
 SELVAM K, VINOD KUMAR D M, SIRIPURAM R. Distributed generation planning using peer enhanced multi-objective teaching-learning based optimization in distribution networks[J]. Journal of the Institution of Engineers (India):Series B, 2016, 98(2):203-211.
 SAHU R K, PANDA S, ROUT U K, et al. Teaching learning based optimization algorithm for automatic generation control of power system using 2-DOF PID controller[J]. International Journal of Electrical Power & Energy Systems, 2016, 77:287-301.
 RAMA KRISHNA P V, SAO S. An improved TLBO algorithm to solve profit based unit commitment problem under deregulated environment[J]. Procedia Technology, 2016, 25:652-659.
 RAO R V, PATEL V. Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems[J]. International Journal of Industrial Engineering Computations, 2013, 4(1):29-50.
 OUYANG H, WANG Q, KONG X. Modified teaching-learning based optimization for 0-1 knapsack optimization problems[C]//Proceedings of the 29th Chinese Control and Decision Conference. Piscataway, NJ:IEEE, 2017:973-977.
 王培崇.改进的动态自适应学习教与学优化算法[J].计算机应用,2016,36(3):708-712. (WANG P C. Improved dynamic self-adaptive teaching-learning-based optimization algorithm[J]. Journal of Computer Applications, 2016, 36(3):708-712.)
 ZHANG H, LI B, ZHANG J, et al. Parameter estimation of nonlinear chaotic system by improved TLBO strategy[J]. Soft Computing, 2016, 20(12):4965-4980.
 DASHTI S E, RAHMANI A M. Dynamic VMs placement for energy efficiency by PSO in cloud computing[J]. Journal of Experimental & Theoretical Artificial Intelligence, 2016, 28(1/2):97-112.
 GARRO B A, RODRÍGUEZ K, VÁZQUEZ R A. Classification of DNA microarrays using artificial neural networks and ABC algorithm[J]. Applied Soft Computing, 2016, 38:548-560.
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