A comparative study on particle swarm optimization and genetic algorithms for fixed order controller design

Abstract

This article deals with a performance evaluation of particle swarm optimization (PSO) and genetic algorithms (GA) for fixed order controller design. The major objective of the work is to compare the ability, computational effectiveness and efficiency to solve the optimization problem for both algorithms (PSO and GA). All simulation has been performed using a software program developed in the Matlab environment. As yet, overall results show that genetic algorithms generally can find better solutions compared to the PSO algorithm. The primary contribution of this paper is to evaluate the two algorithms in the tuning of proportional integral and derivative (PID)-controllers and minimization of cost function and maximization of robust stability in the servo system which represents a complex system. Such comparative analysis is very important for identifying both the advantages and their possible disadvantages. © 2012 Springer-Verlag.