Parameters Optimization of GM(1, 1) Model Based on Artificial Fish Swarm Algorithm

Purpose – The purpose of this paper is to enhance the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.Design/methodology/approach – An optimization model of GM(1,1) model about identifying the parameters is proposed, which takes the minimum of the average relative error as objective function and takes the development coefficient and grey action quantity as decision variables, then an improved artificial fish swarm algorithm is designed to solve the optimization model.Findings – The results show that the proposed method may enhance the precision of GM(1,1) model, and have better performance than particle swarm optimization.Practical implications – The method exposed in the paper can be used to optimize the parameters of GM(1,1) model, which is used frequently to solve the economic and management problem.Originality/value – The paper succeeds in enhancing the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.

[1]  Okyay Kaynak,et al.  Grey system theory-based models in time series prediction , 2010, Expert Syst. Appl..

[2]  Zhang Qi-shan.  Improving the Precision of GM(1,1) Model by Using Particle Swarm Optimization , 2007 .

[3]  Chi-Sheng Shih,et al.  Grey number prediction using the grey modification model with progression technique , 2011 .

[4]  HE Wen-zhang,et al.  Estimation of grey model parameter based on genetic algorithm , 2005 .

[5]  Xia Wang,et al.  The construction of new weakening buffer operators and their application , 2011, Grey Syst. Theory Appl..

[6]  Zhang Xiaowei.  Application of Direct Grey Model to Yearly Electricity Demand Forecasting Based on PSO , 2010 .

[7]  Zheng Zhao.  Direct Modeling Improved GM (1,1) Model IGM (1,1) by Genetic Algorithm , 2003 .

[8]  Wang Hai-feng.  The Application of Particle Swarm Optimization Based Grey Model to Power Load Forecasting , 2007 .

[9]  Hsing-Chih Tsai,et al.  Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior , 2011, Appl. Soft Comput..

[10]  Xinping Xiao,et al.  Research on generalized non-equidistance GM(1, 1) model based on matrix analysis , 2011, Grey Syst. Theory Appl..

[11]  Li Xiao,et al.  An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm , 2002 .

[12]  Shi Qiu-hong.  Global Edition Artificial Fish Swarm Algorithm , 2009 .

[13]  Liu Si-feng,et al.  The GM models that x(n) be taken as initial value , 2004 .

[14]  Ana Maria A. C. Rocha,et al.  An augmented Lagrangian fish swarm based method for global optimization , 2011, J. Comput. Appl. Math..

[15]  Si-feng Liu,et al.  Optimization of Background Value in GM(1,1) Model , 2008 .

[16]  Xinhai Kong,et al.  A kind of universal constructor method for buffer operator , 2011, Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services.

[17]  Wenzhan Dai,et al.  New buffer operators with variable weight based on average tempo and their optimization , 2011, Grey Syst. Theory Appl..

[18]  Yu Dong-mei.  Simplified Artificial Fish Swarm Algorithm , 2009 .

[19]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .

[20]  Chao Wu,et al.  Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm , 2011, Knowl. Based Syst..

[21]  Grey Unbiased GRM(1,1) Model Based on Accumulated Generating Operation in Reciprocal Number and its Application , 2011 .