TY - JOUR
T1 - A novel atom search optimization for dispersion coefficient estimation in groundwater
AU - Zhao, Weiguo
AU - Wang, Liying
AU - Zhang, Zhenxing
N1 - Funding Information:
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 ), and Scientific Research Key Project of University of Hebei Province of China ( ZD2017017 ). Weiguo Zhao received the Ph.D. degree from School of Electrical Engineering, Hebei University of Technology in 2016. Currently he is an associate professor in Hebei University of Engineering, his research interests include intelligent computing, intelligent fault diagnosis and water dynamics. Liying Wang received the Ph.D. degree from Beijng Jiaotong University, China in 2014. Now she is an associate professor and works in Hebei University of Engineering. Her current research interests include intelligent computing, control systems engineering and water dynamics. Zhenxing Zhang is a hydrologist with the Prairie Research Institute, University of Illinois at Urbana–Champaign, focusing on integrate water management, surface water supply, water availability, hydrologic modeling and stochastic hydrology. Dr. Zhang receives his Bachelors of Science in environment science from Wuhan University, Masters of Science in environment science from Peking University, and Masters of Science in applied statistics from University of Syracuse. He holds a Ph.D. degree in water resources engineering from the State University of New York College of Environmental Science and Forestry (SUNY ESF). He is a licensed Professional Engineer (P.E). He has published over 30 papers in top journals including Water Resources Research, Journal of Hydrology, Applied Energy, and Desalination.
PY - 2019/2
Y1 - 2019/2
N2 - A new type of meta-heuristic global optimization methodology based on atom dynamics is introduced. The proposed Atom Search Optimization (ASO) approach is a population-based iterative heuristic global optimization algorithm for dealing with a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact with each other through interaction forces resulting form Lennard-Jones potential and constraint forces resulting from bond-length potential, the algorithm is simple and easy to implement. ASO is applied to a dispersion coefficient estimation problem, the experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) and that ASO is competitive to its competitors for parameter estimation problems. The source codes of ASO are available at https://www.mathworks.com/matlabcentral/fileexchange/67011-atom-search-optimization–aso–algorithm?s_tid=srchtitle.
AB - A new type of meta-heuristic global optimization methodology based on atom dynamics is introduced. The proposed Atom Search Optimization (ASO) approach is a population-based iterative heuristic global optimization algorithm for dealing with a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact with each other through interaction forces resulting form Lennard-Jones potential and constraint forces resulting from bond-length potential, the algorithm is simple and easy to implement. ASO is applied to a dispersion coefficient estimation problem, the experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) and that ASO is competitive to its competitors for parameter estimation problems. The source codes of ASO are available at https://www.mathworks.com/matlabcentral/fileexchange/67011-atom-search-optimization–aso–algorithm?s_tid=srchtitle.
KW - ISWS
KW - Global optimization
KW - Metaheuristic
KW - Optimization algorithm
KW - Parameter estimation
KW - Heuristic algorithm
KW - Atom search optimization
KW - Dispersion coefficient
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U2 - 10.1016/j.future.2018.05.037
DO - 10.1016/j.future.2018.05.037
M3 - Article
SN - 0167-739X
VL - 91
SP - 601
EP - 610
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -