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.

DOI: 10.1155/2015/292576

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Cite this paper

@inproceedings{Wu2015NonlinearIW, title={Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem}, author={Zong-Sheng Wu and Wei-Ping Fu and Ru Xue}, booktitle={Comp. Int. and Neurosc.}, year={2015} }