Drawing Code for Mathematical Benchmark Functions
Versión 1.0.0 (328 KB) por
Mehdi Ghasri
Drawing function with different color maps
Mathematical Benchmark Functions:
- : Unimodal standard functions (F1-F6): To measure the exploitability of an algorithm.
- : Multimodal functions (F6-F13): To test the exploration performance.
- : Fixed-dimensional functionals (F14-F23): To demonstrate the ability to explore in low dimensions.
Stages of implementing the code:
2.![](data:image/png;base64,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)
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Mehdi Ghasri (2025). Drawing Code for Mathematical Benchmark Functions (https://www.mathworks.com/matlabcentral/fileexchange/125645-drawing-code-for-mathematical-benchmark-functions), MATLAB Central File Exchange. Recuperado .
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