Bacterial Foraging-Tabu Search Metaheuristics for Identification of Nonlinear Friction Model

This paper proposes new metaheuristic algorithms for an identification problem of nonlinear friction model. The proposed cooperative algorithms are formed from the bacterial foraging optimization (BFO) algorithm and the tabu search (TS). The paper reports the search comparison studies of the BFO, the TS, the genetic algorithm (GA), and the proposed metaheuristics. Search performances are assessed by using surface optimization problems. The proposed algorithms show superiority among them. A real-world identification problem of the Stribeck friction model parameters is presented. Experimental setup and results are elaborated.

[1]  Sarawut Sujitjorn,et al.  Harmonic Identification for Active Power Filters Via Adaptive Tabu Search Method , 2004, KES.

[2]  B. Abdelhadi,et al.  Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters , 2005, IEEE Transactions on Energy Conversion.

[3]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[4]  Xiongxiong He,et al.  Modeling identification of power plant thermal process based on PSO algorithm , 2005, Proceedings of the 2005, American Control Conference, 2005..

[5]  Deacha Puangdownreong,et al.  Adaptive Tabu Search and Applications in Engineering Design , 2006, Integrated Intelligent Systems for Engineering Design.

[6]  Sarawut Sujitjorn,et al.  Learning Control Via Neuro-Tabu-Fuzzy Controller , 2006, KES.

[7]  Alessandro Corsini,et al.  Inverse parameter identification technique using PSO algorithm applied to geotechnical modeling , 2008 .

[8]  B. Armstrong-Hélouvry.  Stick slip and control in low-speed motion , 1993, IEEE Trans. Autom. Control..

[9]  Iti Saha Misra,et al.  IMPROVED ADAPTIVE BACTERIA FORAGING ALGORITHM IN OPTIMIZATION OF ANTENNA ARRAY FOR FASTER CONVERGENCE , 2008 .

[10]  Ligang Liu,et al.  A Variable Step-Size Proportionate Affine Projection Algorithm for Identification of Sparse Impulse Response , 2009, EURASIP J. Adv. Signal Process..

[11]  P. Patrick Wang,et al.  Parameter structure identification using tabu search and simulated annealing , 1996 .

[12]  Robert Babuška,et al.  Genetic algorithms for optimization in predictive control , 1997 .

[13]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[14]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[15]  Q. Henry Wu,et al.  Bacterial Foraging Algorithm for Optimal Power Flow in Dynamic Environments , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  K. Passino,et al.  Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors , 2002 .

[17]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[18]  K. Fahd,et al.  Optimal Power Flow Using Tabu Search Algorithm , 2002 .

[19]  Vahid Tabataba Vakili,et al.  Modified Particle Swarm Optimization for Blind Deconvolution and Identification of Multichannel FIR Filters , 2010, EURASIP J. Adv. Signal Process..

[20]  Y. A. Kochetov,et al.  Probabilistic Tabu Search Algorithm for the Multi-Stage Uncapacitated Facility Location Problem , 2001 .

[21]  Francesco Alonge,et al.  Least squares and genetic algorithms for parameter identification of induction motors , 2001 .

[22]  David J. Murray-Smith,et al.  Submarine manoeuvring controllers’ optimisation using simulated annealing and genetic algorithms , 2006 .

[23]  Roberto Battiti,et al.  The Reactive Tabu Search , 1994, INFORMS J. Comput..

[24]  David J. Murray-Smith,et al.  Nonlinear model structure identification using genetic programming , 1998 .

[25]  Sukumar Mishra,et al.  A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation , 2005, IEEE Transactions on Evolutionary Computation.

[26]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

[27]  I. Nishikawa,et al.  Line balancing using a genetic evolution model , 1995 .

[28]  Ganapati Panda,et al.  Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques , 2009, Expert Syst. Appl..

[29]  Carlos Canudas de Wit,et al.  A survey of models, analysis tools and compensation methods for the control of machines with friction , 1994, Autom..

[30]  Sukumar Mishra,et al.  Transmission Loss Reduction Based on FACTS and Bacteria Foraging Algorithm , 2006, PPSN.

[31]  Yunlong Zhu,et al.  Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning , 2010, Appl. Soft Comput..

[32]  Satish S. Nair,et al.  Modeling and compensation of low-velocity friction with bounds , 1999, IEEE Trans. Control. Syst. Technol..

[33]  Carlos Canudas de Wit,et al.  A new model for control of systems with friction , 1995, IEEE Trans. Autom. Control..

[34]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[35]  Magdalene Marinaki,et al.  Fuzzy control optimized by PSO for vibration suppression of beams , 2010 .

[36]  N. Sriyingyong,et al.  Wavelet-Based Audio Watermarking Using Adaptive Tabu Search , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[37]  X. He,et al.  Aquifer Parameter Identification with Ant Colony Optimization Algorithm , 2009, 2009 International Workshop on Intelligent Systems and Applications.

[38]  Guimei Zhang,et al.  Improving the structure of deep frozen and chilled food chain with tabu search procedure , 2003 .

[39]  E. Nowicki,et al.  A fast tabu search algorithm for the permutation flow-shop problem , 1996 .

[40]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.