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.
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