Simplex noise

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Simplex noise is the result of an n-dimensional noise function comparable to Perlin noise ("classic" noise) but with fewer directional artifacts, in higher dimensions, and a lower computational overhead. Ken Perlin designed the algorithm in 2001 to address the limitations of his classic noise function, especially in higher dimensions. The advantages of simplex noise over Perlin noise: Simplex noise has lower computational complexity and requires fewer multiplications. Simplex noise scales to higher dimensions (4D, 5D) with much less computational cost: the complexity is O ( n 2 ) {\displaystyle O(n^{2})} for n {\displaystyle n} dimensions instead of the O ( n 2 n ) {\displaystyle O(n\,2^{n...

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