Evolutionary multi-objective optimization of particle swarm optimizers

One issue in applying Particle Swarm Optimization (PSO) is to And a good working set of parameters. The standard settings often work sufficiently but don't exhaust the possibilities of PSO. Furthermore, a trade-off between accuracy and computation time is of interest for complex evaluation functions. This paper presents results for using an EMO approach to optimize PSO parameters as well as to And a set of trade-offs between mean fitness and swarm size. It is applied to four typical benchmark functions known from literature. The results indicate that using an EMO approach simplifies the decision process of choosing a parameter set for a given problem.