NDLI: A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction

Content Provider	Springer Nature Link
Author	Wang, Lei Zou, Feng Hei, Xinhong Yang, Dongdong Chen, Debao Jiang, Qiaoyong Cao, Zijian
Copyright Year	2014
Abstract	Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching–learning-based optimization (TLBO)–differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO–DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.
Starting Page	1407
Ending Page	1422
Page Count	16
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	25
Issue Number	6
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-06-10
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Chaotic time series Hybrid optimization Teaching–learning-based optimization Differential evolution Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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