An Improved Discrete Particle Swarm Optimization Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 219)

Abstract

Evolutionary algorithms solve complex identification problems because they do not take into account any constraint on the cost function and more flexibility is offered when the model structure is chosen and the cost function is minimized. Step input-based identification has been developed, which could be used to determine the transient behavior of a system rapidly. In this paper, a new discrete particle swarm optimization (DPSO) algorithm is presented and the performance of this algorithm for solving identification problems is compared with the GA. The obtained results demonstrate that the proposed DPSO algorithm performs rather well in terms of speed (FNGC) and reaching to the minimum cost.

Keywords

Genetic algorithm Particle swarm optimization algorithm DPSO 

References

  1. 1.
    Herrero JM, Blasco X, Martínez M, Sanchis J (2002) Identification of continuous processes parameters using genetic algorithms. In: Proceedings of the 10th Mediterranean conference on control and automation, Portugal, vol 12(7), pp 52–59Google Scholar
  2. 2.
    Du W, Du F (2009) CMAC-PID control and new smith predictor for networked control systems. In: 4th IEEE conference on industrial electronics and applications, vol 32(12), pp 969–974Google Scholar
  3. 3.
    Majhi S, Atherton DP (2009) Modified smith predictor and controller for processes with time delay. IEEE Proc Control Theory Appl 146(5):359–366CrossRefGoogle Scholar
  4. 4.
    Söderström T, Stoica P (1989) System identification. Prentice-Hall International, London, vol 74(2), pp 809–813Google Scholar
  5. 5.
    Al-Tabtabai H, Alex PA (1999) Using genetic algorithms to solve optimization problems in construction. Eng Constr Archit Manag 42(5):121–132Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.AnYang Institute of TechnologyAnYangChina

Personalised recommendations