title: Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem.
abstract: Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
authors.affiliation: School of Mechanical and Precision Instrumental Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.
authors.forename: Zong-Sheng
authors.initials: ZS
authors.lastname: Wu
authors.affiliation: School of Mechanical and Precision Instrumental Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.
authors.forename: Wei-Ping
authors.initials: WP
authors.lastname: Fu
authors.affiliation: School of Information Engineering, Tibet University for Nationalities, Xianyang, Shaanxi 712082, China.
authors.forename: Ru
authors.initials: R
authors.lastname: Xue
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D017711:Nonlinear Dynamics; D011340:Problem Solving; D013663:Teaching