Particle swarm optimization of tuned mass dampers

Abstract

Tuned mass dampers (TMD) have been widely used to attenuate undesirable vibrations in engineering. Most optimization problems of TMD are solved by either numerical iteration technique or conventional mathematical methods that require substantial gradient information. The selection of the starting values is very important to ensure convergence. In this paper, we use a novel evolutionary algorithm of particle swarm optimization (PSO) for optimization of the required parameters of a TMD. Optimum parameters of the TMD system attached to a viscously damped single degree-of-freedom main system are obtained by minimizing some response quantities, for examples, the mean square displacement responses and displacement amplitude of the main system under various combinations of different kinds of excitations. The excitations considered include external force and base acceleration modeled as Gaussian white-noise random processes. Harmonic base acceleration with frequency invariant amplitude is also considered. The PSO can be used to find the optimum mass ratio, damper damping and tuning frequency of the TMD system and can be easily programmed for practical engineering applications. Explicit expressions of the optimum TMD parameters are given for engineering designers.