Choose a generator and look at how the random colors are distributed.
The purpose here is to observe the properties of different generators of colors.
Set a small number of colors (10 to 50), generate several times to look if it fits your expectations.
Set a large number of colors (1000 or more), check "scatterplot style", generate and you will observe how the probabilities are distributed (takes more time).
A random value in each red/green/blue channel.
A random value in each hue/saturation/lightness channel.
A random value in each hue/saturation/value channel.
Random numbers do not necessary make nice swatches. You may read sometimes that HSV random colors are better than RGB. I think that it is clearly not the case.
These basic generators allow to see that the "randomness" strongly depends on the color space that is randomized.
A random value in each L*/a*/b* color coordinates (fits to how we perceive colors).
A random value in each L*/a*/b* color coordinates, filtered to keep only pastel colors.
A random value in each L*/a*/b* color coordinates, filtered to keep only flashy colors.
The L*a*b* color space is the more natural to the eye. Randomizing in this space gives more natural results.
Adding constraints helps to set some properties of the produced palette ('Pimp', 'Pastel'), and keeps the natural distribution of the colors.
A Force Vector algorithm optimizes the distribution of colors inthe RGB color space.
A Force Vector algorithm optimizes the distribution of colors inthe L*a*b* color space.
A Force Vector algorithm optimizes the distribution of colors inthe L*a*b* color space. Additional conditions apply to restrain the color space to flashy colors.
The Force Vector strategy allows an even distribution of the colors in the space. It still allows some coontrol on the produced palette ('Fancy').
Note that contrary to the "naïve" generators, the colors are generated all at the same time (they depend on each other).
The k-Means algorithm clusterises the RGB color space. The resulting palette is constituted of these k-Means (the "centers" of color clusters).
The k-Means algorithm clusterises the L*a*b* color space. The resulting palette is constituted of these k-Means (the "centers" of color clusters).
Idem, but with an additionnal constraint (high chroma and lightness*).
This strategy also allows an even distribution of the colors, but with less "extreme" colors (the borders of the color space are not occupied). It allows more coordinated colors on small palettes.
NB: The palette strongly depends on the number of colors asked.
RGB or "Red Green Blue": the most easy to compute since it fits to how computer screens and TV work. Actually it fits quite well to our perception.
HSV or "Hue Saturation Value": used in softwares like Photoshop, since it allows to separate the hue (the color itself) for other parameters. It allows strong perceptual biases since the hues are not perceived with the same lightness (yellow is light, blue is dark...).
HSL or "Hue Saturation Lightness" is a variant of this space, and is biased as well.
L*a*b* or "Lightness* a* b*" is a space designed to fit to our perception. Lightness* is the unbiased version of lightness (a scale from black to color to white) and the a* and b* dimensions describe the colors (and grays). It is not easy to deal with it, since every color does not exist, but it gives the most appropriate results.
HCL or "Hue Chroma Lightness*" is a transformation of L*a*b* where the Hue has been separated. It can be considered as the unbiased version of HSL, where saturation is named Chroma and the unbiased version of Lightness is Lightness* (the star indicates the difference). Like with L*a*b*, every color does not exist. What's more, it is not a convex space, and this is an issue for computing colors. That's why I prefer L*a*b* to compute, but HCL is the best space to monitor results.
The squares above are projections of different color spaces. Example: the RGB space is represented with R x G, G x B and R x B. They allow you to determine where the colors accumulate and where they are sparse. For instance you will observe that an even distribution in a given space (say, HSV) can be uneven in another space (RGB).
We assume that our goal is to generate varied colors. So we want to avoid accumulation, since it means that some colors are too similar, thus the palette is confusing.
The most suitable palettes are probably the ones that are evenly distributed in the L*a*b* color space.
The sorted palette contain the exact same colors (the ones that were generated) but sorted different ways.
The goal is just to check if there are similar colors. Sorting the colors allows to put similar colors together. For instance if two shades of blue are very similar, they will be put together in the "Hue" palette.
Here are the colors sorted so that the most different colors are put first. This is very useful in many cases, since we often know what we want to differentiate in priority.