Atom search optimization and its application to solve a hydrogeologic parameter estimation problem

Weiguo Zhao, Liying Wang, Zhenxing Zhang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)283-304
Number of pages22
JournalKnowledge-Based Systems
StatePublished - Jan 1 2019


  • Atom search optimization
  • Benchmark functions
  • Global optimization
  • Heuristic algorithm
  • Metaheuristic
  • Optimization algorithm
  • Parameter estimation

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence


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