English: Performance landscape showing how a simple Particle Swarm Optimization (PSO) variant performs in aggregate on several benchmark problems when varying two PSO parameters. Lower meta-fitness values means better PSO performance. Such a performance landscape is very time-consuming to compute, especially for optimizers with several behavioural parameters, but it can be searched efficiently using the simple meta-optimization approach by Pedersen implemented in SwarmOps to uncover PSO parameters with good performance. Good choices would here seem to be in the region ${\displaystyle \omega \in [-0.6;0]}$ and ${\displaystyle \phi _{g}\in [2;4]}$, and the region ${\displaystyle \omega \in [-1.25;-0.75]}$ and ${\displaystyle \phi _{g}\in [-3.5;-2]}$