Wu Zong-Sheng, Fu Wei-Ping, Xue Ru
School of Mechanical and Precision Instrumental Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.
School of Information Engineering, Tibet University for Nationalities, Xianyang, Shaanxi 712082, China.
Comput Intell Neurosci. 2015;2015:292576. doi: 10.1155/2015/292576. Epub 2015 Sep 2.
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.
教学学习优化（TLBO）算法是近年来提出的，它模拟课堂教学学习现象，以有效解决连续空间中多维、线性和非线性问题的全局优化。本文提出了一种改进的教学学习优化算法，即非线性惯性加权教学学习优化（NIWTLBO）算法。该算法在基本TLBO中引入非线性惯性加权因子来控制学习者的记忆率，并使用动态惯性加权因子替代教师阶段和学习者阶段中的原始随机数。该算法在多个基准函数上进行了测试，并与基本TLBO和其他一些著名优化算法进行了性能比较。实验结果表明，该算法比基本TLBO和其他一些算法具有更快的收敛速度和更好的性能。
