PSO-Based Parametric Modelling of a Thin Plate Structure

Parametric modelling of dynamic structure may benefits from features of particle swarm optimization (PSO), which robust and fast in solving nonlinear, non-differentiable, and multimodal problems. This paper presents the PSO approach which includes an improved algorithm to model a flexible plate structure parametrically. The introduction of a dynamic spread and momentum factor, both by modifying the inertial weight of each particle, solves the problem of getting stuck at local optima, preserves diversity and trades-off between exploration and exploitation. The identification is performed on basis of minimizing the mean-squared output error and is assessed with correlation tests and in time and frequency domains. It is shown input-output characterization in time and frequency domains that the improved algorithm possesses features of accuracy and quick convergence.

[1]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[2]  W ReynoldsCraig.  Flocks, herds and schools: A distributed behavioral model , 1987 .

[3]  Chunming Yang,et al.  A new particle swarm optimization technique , 2005, 18th International Conference on Systems Engineering (ICSEng'05).

[4]  Shangxu Hu,et al.  A New Approach to Improve Particle Swarm Optimization , 2003, GECCO.

[5]  M. Osman Tokhi,et al.  Dynamic modelling of a single-link flexible manipulator: parametric and non-parametric approaches , 2002, Robotica.

[6]  M. Osman Tokhi,et al.  Fast convergence strategy for Particle Swarm Optimization using spread factor , 2009, 2009 IEEE Congress on Evolutionary Computation.

[7]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[8]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  Thomas Weise,et al.  Global Optimization Algorithms -- Theory and Application , 2009 .

[10]  Mohamed A. El-Sharkawi,et al.  Fundamentals of Particle Swarm Optimization Techniques , 2008 .

[11]  Xiaohua Liu,et al.  Solving multi objective optimization problems using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[12]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[13]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[14]  Siti Fauziah Toha,et al.  Parametric Modelling of a TRMS Using Dynamic Spread Factor Particle Swarm Optimisation , 2009, 2009 11th International Conference on Computer Modelling and Simulation.

[15]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[17]  Pascal Bouvry,et al.  Modern Metaheuristics for Function Optimization Problem , 2005, Intelligent Information Systems.

[18]  Yanchun Liang,et al.  An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.