Atom search optimization and its application to solve a hydrogeologic parameter estimation problem
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 s...
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| Vydáno v: | Knowledge-based systems Ročník 163; s. 283 - 304 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.01.2019
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0950-7051, 1872-7409 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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.
•A novel optimization algorithm called Atom Search Optimization (ASO) is proposed.•ASO is benchmarked on 37 well-known test functions.•The results on test functions show the competitiveness of ASO.•The results on hydrogeologic parameter estimation confirm the performance of ASO. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0950-7051 1872-7409 |
| DOI: | 10.1016/j.knosys.2018.08.030 |