Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications

Abstract

The Shuffled Complex Evolution (SCE-UA) method developed at the University of Arizona is a global optimization algorithm, initially developed by [1] for the calibrationof conceptual rainfall-runoff (CRR) models. SCE-UA searches for the global optimumof a function by evolving clusters of samples drawn from the parameter space, via a systematiccompetitive evolutionary process. Being a general purpose global optimization algorithm, it has found widespread applications across a diverse range of science and engineering fields. Here, we recount the history of the development of the SCE-UA algorithm and its later advancements. We also present a survey of illustrative applications of the SCE-UA algorithm and discuss its extensions to multi-objective problems and touncertainty assessment. Finally, we suggest potential directions for future investigation.