Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem

• Main Authors: Zong-Sheng Wu, Wei-Ping Fu, Ru Xue
• Format: Article
• Language: English
• Published: Hindawi Limited 2015-01-01
• Series: Computational Intelligence and Neuroscience
• Online Access: http://dx.doi.org/10.1155/2015/292576

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.