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Atom search optimization and its application to solve a hydrogeologic parameter estimation problem
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DOI：10.1016/j.knosys.2018.08.030.png)
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Abstract
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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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Optimization algorithm
Heuristic algorithm
Benchmark functions
Atom search optimization
Global optimization
Metaheuristic
Parameter estimation
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