A novel atom search optimization for dispersion coefficient estimation in groundwater
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...
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| Published in: | Future generation computer systems Vol. 91; pp. 601 - 610 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.02.2019
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| Subjects: | |
| ISSN: | 0167-739X, 1872-7115 |
| Online Access: | Get full text |
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| Abstract | 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.
•A novel optimization algorithm called Atom Search Optimization (ASO) is proposed.•The ASO is applied to dispersion coefficient estimation problem in groundwater.•The results on dispersion coefficient estimation confirm the competitiveness of ASO. |
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| AbstractList | 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.
•A novel optimization algorithm called Atom Search Optimization (ASO) is proposed.•The ASO is applied to dispersion coefficient estimation problem in groundwater.•The results on dispersion coefficient estimation confirm the competitiveness of ASO. |
| Author | Wang, Liying Zhao, Weiguo Zhang, Zhenxing |
| Author_xml | – sequence: 1 givenname: Weiguo surname: Zhao fullname: Zhao, Weiguo email: wgzhao@illinois.edu organization: School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, Hebei, 056021, China – sequence: 2 givenname: Liying surname: Wang fullname: Wang, Liying email: wangliying@hebeu.edu.cn organization: School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, Hebei, 056021, China – sequence: 3 givenname: Zhenxing surname: Zhang fullname: Zhang, Zhenxing email: zhang538@illinois.edu organization: Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana–Champaign, Champaign, IL 61820, USA |
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| SubjectTerms | Atom search optimization Dispersion coefficient Global optimization Heuristic algorithm Metaheuristic Optimization algorithm Parameter estimation |
| Title | A novel atom search optimization for dispersion coefficient estimation in groundwater |
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