A novel atom search optimization for dispersion coefficient estimation in groundwater

Weiguo Zhao, Liying Wang, Zhenxing Zhang

Research output: Contribution to journalArticle

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) or Bacterial Foraging Optimization (BFO) and that ASO is competitive with its competitors for parameter estimation problems.
LanguageEnglish (US)
Pages601-610
Number of pages10
JournalFuture Generation Computer Systems
Volume91
DOIs
StatePublished - 2019
Externally publishedYes

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Groundwater
Atoms
Global optimization
Lennard-Jones potential
Bond length
Set theory
Parameter estimation
Particle swarm optimization (PSO)
Genetic algorithms

Keywords

  • ISWS

Cite this

A novel atom search optimization for dispersion coefficient estimation in groundwater. / Zhao, Weiguo; Wang, Liying; Zhang, Zhenxing.

In: Future Generation Computer Systems, Vol. 91, 2019, p. 601-610.

Research output: Contribution to journalArticle

Zhao, Weiguo ; Wang, Liying ; Zhang, Zhenxing. / A novel atom search optimization for dispersion coefficient estimation in groundwater. In: Future Generation Computer Systems. 2019 ; Vol. 91. pp. 601-610.
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