A novel atom search optimization for dispersion coefficient estimation in groundwater

Weiguo Zhao, Liying Wang, Zhenxing Zhang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)601-610
Number of pages10
JournalFuture Generation Computer Systems
Volume91
DOIs
StatePublished - Feb 2019

Keywords

  • ISWS
  • Global optimization
  • Metaheuristic
  • Optimization algorithm
  • Parameter estimation
  • Heuristic algorithm
  • Atom search optimization
  • Dispersion coefficient

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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