TY - JOUR
T1 - Three decades of the shuffled complex evolution (sce-ua) optimization algorithm
T2 - Review and applications
AU - Naeini, M. Ralinamay
AU - Analui, B.
AU - Gupta, H. V.
AU - Duan, Q.
AU - Soroosliian, S.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The Shuffled Complex Evolution (SCE-UA) method developed at the University of Arizona is a global optimizat ion algorithm, initially developed by Duan et. al. [Duan, ()., Sorooshian, S., and Gupta, V. "Effective and efficient global optimization for conceptual rainfall-runoff models", Water Resources Research.4 28(4), pp. 1015-1031 (1992)]. for the calibration of Conceptual Rainfall-Runoff (CRR) models. SCE-UA searches for the global optimum of a function by evolving clusters of samples drawn from the parameter space, via a systematic competitive 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 to uncertainty assessment. Finally, we suggest potential directions for future investigation.
AB - The Shuffled Complex Evolution (SCE-UA) method developed at the University of Arizona is a global optimizat ion algorithm, initially developed by Duan et. al. [Duan, ()., Sorooshian, S., and Gupta, V. "Effective and efficient global optimization for conceptual rainfall-runoff models", Water Resources Research.4 28(4), pp. 1015-1031 (1992)]. for the calibration of Conceptual Rainfall-Runoff (CRR) models. SCE-UA searches for the global optimum of a function by evolving clusters of samples drawn from the parameter space, via a systematic competitive 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 to uncertainty assessment. Finally, we suggest potential directions for future investigation.
KW - Evolutionary algorit hm
KW - Hydrology
KW - Mult i-object ive
KW - Optimizat ion
KW - Shuffled complex evolution, sce-ua
KW - Uncertainty assessment
KW - Water resources
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U2 - 10.24200/sci.2019.21500
DO - 10.24200/sci.2019.21500
M3 - Review article
AN - SCOPUS:85073471708
VL - 26
SP - 2015
EP - 2031
JO - Scientia Iranica
JF - Scientia Iranica
SN - 1026-3098
IS - 4A
ER -