Dynamical Adaptive Particle Swarm Algorithm and Its Application to Optimization of PID Parameters

Jimin Li, Guolin Yu

Research Institute of Information and System Computation Science, The North University for Nationalities, Yinchuan, China.

DOI: 10.4236/ajor.2012.23053                

Abstract

Based on a new adaptive Particle Swarm Optimization algorithm with dynamically changing inertia weight (DAPSO), It is used to optimize parameters in PID controller. Compared to conventional PID methods, the simulation shows that this new method makes the optimization perfectly and convergence quickly.

Keywords

Particle Swarm Optimization; Dynamical Adaptive; PID Automatic Regulation System

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Kennedy and R. Eberhert, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, IEEE Service Center Press, IV. Piscataway, New Jersey, 1995, pp. 1942-1948.
[2]	C. Elegbede, “Structural Reliability Assessment Based on Particles Swarm Optimization,” Structural Safety, Vol. 27, No. 2, 2005, pp. 171-186. doi:10.1016/j.strusafe.2004.10.003
[3]	J. Pobinson and Y. Rahmat-Samii, “Particle Swarm Optimization in Electromagnetics,” IEEE Transactions on Antennas and Propagation, Vol. 52, No. 2, 2004, pp. 397-406. doi:10.1109/TAP.2004.823969
[4]	A. Salman, I. Ahmad and S. Al-Madani, “Particle Swarm Optimization for Task Assignment Problem,” Microprocessors and Microsystems, Vol. 26, No. 8, 2002, pp. 363-371. doi:10.1016/S0141-9331(02)00053-4
[5]	Y. Shi and R. Eberhart, “Empirical Study of Particle Swarm Optimization,” International Conference on Evolutionary Computation, IEEE Service Center Press, Washington DC, 1999, pp. 1945-1950.
[6]	Y. Shi and R. Eberhart, “Fuzzy Adaptive Particle Swarm Optimization,” The IEEE Congress on Evolutionary Compution, IEEE Service Center Press, San Francisco, 2001, pp. 101-106.
[7]	R. Eberhart and Y. Shi, “Tracking and Optimizing Dynamic Systems with Particle Swarm,” The IEEE Congress on Evolutionary Computation, IEEE Service Center Press, San Francisco, 2001, pp. 94-100.
[8]	J. M. Li, C. M. Lei and Y. Qiao, “Based on Expectations of Survival Rate Dynamic Adaptive Particle Swarm Algorithm,” Journal of Ningxia University, Vol. 12, 2009, pp. 347-350.
[9]	Y. X. Yuan and W. Y. Sun, “Optimization Theory and Method,” Science Press, Beijing, 1999, pp. 69-75.