Particle swarm optimization - Mass-spring system analogon

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 massspring system is developed and tested against other stochastic a gorithms. 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.