多种群遗传算法在篦冷机二次风温预测中的应用

doi:10.3969/j.issn.1000-1158.2019.02.13

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摘要针对GA遗传算法种群多样性差、局部寻优能力差等问题,提出了多种群遗传算法(MGA)。该算法利用间断平衡理论,构建多种群、多交叉算子操作方式并结合局部搜索方法和种群动态调整策略,提高算法的局部寻优能力和寻优速度。通过与GA和ISGA算法相比,MGA运行时间短,搜索性能强。利用MGA优化MKLSSVM参数,建立基于MGA-MKLSSVM的水泥篦冷机二次风温预测模型。结果表明,此模型辨识精度高、泛化能力强。

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	刘彬
	张春燃
	孙超
	顾昕峰
	刘浩然

关键词 ：计量学, 多种群遗传算法, 二次风温, 多交叉算子, 局部搜索, 篦冷机

Abstract：Aiming at the problem of poor diversity and poor local optimization ability of the genetic algorithm, a multi-population genetic algorithm (MGA) was proposed. According to the theory of Punctuated Equilibria, the algorithm took the manipulation of multiple populations, and multiple crossover operators. Meanwhile, the MGA algorithm had also composed of the local search method and population dynamics adjustment strategy to improve the local search ability and speed. Compared with GA and ISGA algorithms, MGA running time is short and has a better optimization performance. Finally, the MGA algorithm was applied to optimize the multi-kernal least square vector mechaine(MKLSSVM) parameters. And then established the secondary air temperature model of the cement grate cooler based on MGA-MKLSSVM. The results show that this model has high recognition precision and strong generalization ability.

Key words： metrology multi-population genetic algorithm secondary air temperature multiple crossover operators local search grate coller

收稿日期: 2017-09-11 发布日期: 2019-03-07

PACS:	TB94
	TB973

基金资助:国家自然科学基金(51641609);河北省自然科学基金项目(F2016203354)

通讯作者: 张春燃为本文通讯作者。Email:zhangchunran_2015@stumail.ysu.edu.cn E-mail: zhangchunran_2015@163.com

作者简介: 刘彬(1953-),黑龙江哈尔滨人,燕山大学电气工程学院教授,主要从事信号估计与识别算法、智能传感与无线传感器网络关键技术的研究。Email: liubin@ysu.edu.cn

引用本文:

刘彬,张春燃,孙超,顾昕峰,刘浩然. 多种群遗传算法在篦冷机二次风温预测中的应用[J]. 计量学报, 2019, 40(2): 252-258.
LIU Bin,ZHANG Chun-ran,SUN Chao,GU Xin-feng,LIU Hao-ran. The Application of Multi-population Genetic Algorithm in Secondary Air Temperature of Grate Cooler. Acta Metrologica Sinica, 2019, 40(2): 252-258.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2019.02.13 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2019/V40/I2/252

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