Particle swarm optimization - mass-spring system analogon

Abstract

A concept for the optimization of nonlinear cost functionals, occurring in electrical engineering applications, using particle swarm optimization (PSO) is proposed. PSO is a stochastic optimization technique, whose stochastic behavior can be controlled very easily by one single factor. Additionally, this factor can be chosen to end up with a deterministic strategy, that does not need gradient information. The PSO concept is quite simple and easy to implement (just a few code lines are needed). In this paper, an analogy between the movement of a swarm member and a mass-spring system is developed and tested against other stochastic algorithms. It will be shown how infeasible regions in the parameter space can be treated efficiently and, finally, the particular PSO implementation is used to optimize problems occurring in electrical engineering.