The Equilibrium Optimizer (EO) is inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach the equilibrium state (optimal result). A well-defined “generation rate” term is proved to invigorate EO’s ability in exploration,
EO has been designed to solve single-objective optimization problems. This algorithm has been implemented in a wide range of programming languages. You can download the source code at the bottom of this page.