Editing Particle swarm optimization (section)

== Parameter selection ==
[[File:PSO Meta-Fitness Landscape (12 benchmark problems).JPG|thumb|Performance landscape showing how a simple PSO variant performs in aggregate on several benchmark problems when varying two PSO parameters.]]

The choice of PSO parameters can have a large impact on optimization performance. Selecting PSO parameters that yield good performance has therefore been the subject of much research.<ref name=taherkhani2016inertia/><ref name=shi98parameter/><ref name=eberhart00comparing/><ref name=carlisle01offtheshelf/><ref name=bergh01thesis/><ref name=clerc02explosion/><ref name=trelea03particle/><ref name=bratton08simplified/><ref name=evers09thesis/>

To prevent divergence ("explosion") the inertia weight must be smaller than 1. The two other parameters  can be then derived thanks to the constriction approach,<ref name="clerc02explosion" /> or freely selected, but the analyses suggest convergence domains to constrain them. Typical values are in <math>[ 1,3]</math>.

The PSO parameters can also be tuned by using another overlaying optimizer, a concept known as [[meta-optimization]],<ref name="meissner06optimized" /><ref name="pedersen08thesis" /><ref name="pedersen08simplifying" /><ref name="mason2017meta" /> or even fine-tuned during the optimization, e.g., by means of fuzzy logic.<ref name="nobile2017" /><ref name="nobile2015" />

Parameters have also been tuned for various optimization scenarios.<ref name=cazzaniga2015/><ref name=pedersen10good-pso/>