基于混沌分组教与学优化算法锅炉NO<sub>x</sub>模型优化研究

doi:10.3969/j.issn.1000-1158.2018.01.26

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Abstract In order to balance the global and local search ability of the teaching-learning-based optimization, an enhanced teaching-learning-based optimization algorithm is proposed. The proposed algorithm adopts three major adjustment mechanisms. First, the chaos method is applied to initialize population. Second, a self-adaptive inertia weight is adopted to update the individual in teaching phase. Finally, in learning phase, the idea of shuffled leap frog algorithm is applied to group the individuals for updating the worst solution. Ten famous testing functions are applied to test the performance of the proposed algorithm, comparing with ABC, GSA and conventional TLBO. The experiment results show that the improved algorithm own better global and local search ability and high convergence precision. Simultaneously, the proposed algorithm is used to optimize the NOx emissions model of circulating fluidized bed boiler, the experiment result shows that the proposed algorithm possesse better identification ability and generalization, and it also can guide the practical project.

Key words： metrology NO_x TLBO chaos self-adaptive shuffled leap frog algorithm circulating fluidized bed boiler

Received: 01 December 2015 Published: 29 December 2017

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TB99

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	Articles by authors
	MA Yun-peng
	NIU Pei-feng
	CHEN Ke
	YAN Shan-shan
	LI Guo-qiang

Cite this article:

MA Yun-peng,NIU Pei-feng,CHEN Ke, et al. Optimize NO_x Emissions Model of Boiler Based on Chaos Group Teaching-learning-based Optimization Algorithm[J]. Acta Metrologica Sinica, 2018, 39(1): 125-129.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2018.01.26 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2018/V39/I1/125

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