TY - JOUR
T1 - Atom search optimization and its application to solve a hydrogeologic parameter estimation problem
AU - Zhao, Weiguo
AU - Wang, Liying
AU - Zhang, Zhenxing
N1 - Funding Information:
The authors would like to thank Lisa Sheppard of University of Illinois at Urbana–Champaignfor editorial review. This work is supported in part by Natural Science Foundation of Hebei Province of China ( E2018402092 , F2017402142 ), High-Level Talent Funding Project of Hebei Province of China ( B2017003014 ), Engineering Technology Research Centre of Hebei Province of China for High Efficient Utilization of Water Resources ( 18965307H ), and Scientific Research Key Project of University of Hebei Province of China ( ZD2017017 ).
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential. The proposed algorithm is simple and easy to implement. ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success. The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems.
AB - In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential. The proposed algorithm is simple and easy to implement. ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success. The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems.
KW - Atom search optimization
KW - Benchmark functions
KW - Global optimization
KW - Heuristic algorithm
KW - Metaheuristic
KW - Optimization algorithm
KW - Parameter estimation
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U2 - 10.1016/j.knosys.2018.08.030
DO - 10.1016/j.knosys.2018.08.030
M3 - Article
AN - SCOPUS:85053032185
SN - 0950-7051
VL - 163
SP - 283
EP - 304
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
ER -