A New Objective Function for the Recovery of Gielis Curves
| Autor: | Horacio Legal-Ayala, Gabriel Giovanni Caroni, Sebastián Alberto Grillo, Alejandro Marcelo Arce, José Luis Vázquez Noguera, Diego P. Pinto-Roa |
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| Rok vydání: | 2020 |
| Předmět: |
Physics and Astronomy (miscellaneous)
General Mathematics parameter recovery 02 engineering and technology Geometric shape 01 natural sciences 010309 optics Set (abstract data type) Superformula Gielis curves 0103 physical sciences Euclidean geometry Genetic algorithm genetic algorithm Computer Science (miscellaneous) Mathematics lcsh:Mathematics Small number lcsh:QA1-939 021001 nanoscience & nanotechnology Euclidean distance Chemistry (miscellaneous) superformula 0210 nano-technology Algorithm Arc length |
| Zdroj: | Symmetry, Vol 12, Iss 1016, p 1016 (2020) Symmetry Volume 12 Issue 6 |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym12061016 |
| Popis: | The superformula generates curves called Gielis curves, which depend on a small number of input parameters. Recovering parameters generating a curve that adapts to a set of points is a non-trivial task, thus methods to accomplish it are still being developed. These curves can represent a great variety of forms, such as living organisms, objects and geometric shapes. In this work we propose a method that uses a genetic algorithm to minimize a combination of three objectives functions: Euclidean distances from the sample points to the curve, from the curve to the sample points and the curve length. Curves generated with the parameters obtained by this method adjust better to real curves in relation to the state of art, according to observational and numeric comparisons. |
| Databáze: | OpenAIRE |
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