多种群遗传算法在篦冷机二次风温预测中的应用
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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。
Aiming at the problem of poor diversity and poor local optimization ability of the genetic algorithm,a multipopulation 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 MGAMKLSSVM. The results show that this model has high recognition precision and strong generalization ability.
Aiming at the problem of poor diversity and poor local optimization ability of the genetic algorithm,a multipopulation 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 MGAMKLSSVM. The results show that this model has high recognition precision and strong generalization ability.
引文
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[3] Andre J,Siarry P,Dognon T. An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization[J]. Advances in engineering software,2001,32(1):49-60.
[4] Liaw C F. A hybrid genetic algorithm for the open shop scheduling problem[J]. European Journal of Operational Research,2000,124(1):28-42.
[5]巩敦卫,孙晓燕.变搜索区域多种群遗传算法[J].控制理论与应用,2006,23(2):256-260.Gong D W,Sun X Y. Multi-population genetic algorithms with variational search areas[J]. Control Theory&Application,2006,23(2):256-260.
[6]蔡良伟,张基宏,李霞.作业车间调度问题的多种群遗传算法[J].电子学报,2005,33(6):991-994.Cai L W,Zhang J H,Li X. Multi-population genetic algorithm for job shop scheduling problem[J]. Acta Electronica Sinica,2005,33(6):991-994.
[7]刘彬,刘永记,刘浩然,等.基于改进遗传爬山算法的篦冷机熟料换热二次风温故障诊断[J].计量学报,2018,39(5):651-658.Liu B,Liu Y J,Liu H R,et al. Fault Diagnosis of Secondary Air Temperature of Grate Cooler Cement Clinker Heat Transfer Based on Improved Genetic Hill Climbing Algorithm[J]. Acta Metrologica Sinica,2018,39(5):651-658.
[8] Rademaker O,Goessens L H,Voskamp J H, et al.Dynamics and control of a clinker cooler[J]. Automatica,1970,6(2):231-244.
[9]王美琪,刘彬,闻岩,等.篦冷机的渗流传热模型与数值模拟[J].机械工程学报,2014,50(24):152-157.Wang M Q,Liu B,Wen Y,et al. Flow and heat transfer model and numerical simulation of a grate cooler[J].Journal of Mechanical Engineerin,2014,50(24):152-157.
[10] Suykens J A K,Vandewalle J. Least squares support vector machine classifiers[J]. Neural processing letters,1999,9(3):293-300.
[11]吴德会.基于最小二乘支持向量机的传感器非线性动态系统辨识[J].计量学报,2008,29(3):226-230.Wu D H. Identificatio for nonlinear dynamic system of transducer based on least squares support vector machine.[J]. Acta Metrologica Sinica,2008,29(3):226-230.
[12] Aiolii F,Donini M. Easy MKL:a scalable multiple kernel learning algorithm[J]. Neurocomputing,2015,169:215-224.
[13] Nebro A J,Luna F,Alba E,et al. Ab YSS:Adapting Scatter Search to Multiobjective Optimization[J]. IEEE Transactions on Evolutionary Computation,2008,12(4):439-457.
[14] Eshelman L J,Schaffer J D. Real-Coded Genetic Algorithms and Interval-Schemata[J]. Foundations of Genetic Algorithms,1992,2:187-202.
[15] Deb K,Agrawal S,Pratap A,et al. A fast and elitist multi-objective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[16] Tsutsui S,Yamamura M,Higuchi T. Multi-parent recombination with simplex crossover in real coded genetic algorithms[M]. San Francisco:Morgan Kaufmann Publishers Inc,1999:657-664.
[17] Deb K,Anand A,Joshi D. A computationally efficient evolutionary algorithm for real-parameter evolution[J].Evolutionary Computation,2002,10(4):371-395.
[18]赵朋程,刘彬,高伟,等.用于水泥熟料f Ca O预测的多核最小二乘支持向量机模型[J].化工学报,2016,67(6):2480-2487.Zhao P C,Liu B,Gao W,et al. A multi-core least squares support vector machine model for fca O prediction of cement clinker[J]. CIESC Journal,2016,67(6):2480-2487.
[19] Higuchi T,Tsutsui S,Yamamura M. Simplex crossover for real-coded genetic algolithms[J]. Transactions of the Japanese Society for Artificial Intelligence,2001,16(1):147-155.