A novel chaotic teaching learning based optimization algorithm and its application in optimization of extreme learning machine

Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic tea...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-08, Vol.2003 (1), p.12003
Main Authors: Ma, Yunpeng, Tang, Haoheng, Wang, Heqi, Wang, Zhenying, Zhang, Xinxin, Li, Lipeng
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Artificial neural networks
Chaos Theory
Convergence
Extreme Learning Machine
Machine learning
Mathematical problems
Model accuracy
Nitrogen oxides
Optimization
Optimization algorithms
Teaching Learning Based Optimization Algorithm
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Summary:Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic teaching learning based optimization algorithm (called CTLBO) is proposed. Firstly, chaotic variables are applied to initialize population individuals for increasing the diversity of population. Secondly, a kind of self-adaptive acceleration coefficient is introduced into teaching phase to enhance the convergence speed and solution quality. Finally, two population updating mechanisms are proposed to balance the exploration and exploitation capabilities in the learning phase. One is neighbor elitist search mechanism, another is chaos optimization mechanism. The performance of CTLBO is compared with five state-of-the-art optimization algorithms by several CEC mathematical problems. The experiment results show that the CTLBO yields better convergence rate than other algorithms on most testing functions. Additionally, the proposed CTLBO is applied to optimize the model parameters of extreme learning machine(ELM) and the tuned ELM is adopted to establish the NOx emissions model. Experiment results reveal that the NOx emissions model has good accuracy and meets the engineering requirement.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2003/1/012003