Discussing the article: "Color Harmony Algorithm (CHA)"

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Check out the new article: Color Harmony Algorithm (CHA).

We take a closer look at the Color Harmony Algorithm (CHA) — a meta-heuristic optimization method based on Munsell's theory of color harmony. We explore the structure of the hue circle, harmony templates, the alternation between the concentration and dispersion phases, and the role of solution memory. From a theoretical framework to a working implementation in MQL5 and unbiased testing on a standard benchmark.

The Color Harmony Algorithm (CHA), proposed by researchers Zaeimi and Goddosian in 2020, draws on a source of inspiration that seems too far removed from the computational nature of the problem. At first glance, the connection between Munsell's rules for color combinations in painting and numerical optimization of functions seems far-fetched. But if you look closely, the metaphor turns out to be less of an embellishment and more of a structural framework for the entire algorithm — and it is precisely this structure that makes it an interesting subject for analysis.

In standard metaheuristics, a population is a “crowd” in which all solutions are equal and interact according to the same rules. CHA introduces an explicit hierarchy: the population is organized into a hue circle divided into 100 sectors across 10 color groups, with one leader agent in each group; rules governing harmonious combinations apply between the groups. Candidates are constructed not as mutations of individual solutions, but as color combinations — linear mixtures based on strictly defined rules for selecting parents.

From the perspective of a trader or researcher selecting a metaheuristic for their task, CHA has two appealing features. The first is a deliberately structured population: the algorithm does not collapse to a single point through random clustering, because the geometry of the hue circle maintains population diversity by design. The second is explicit switching between the concentration (exploitation) and dispersion (exploration) phases based on a clear criterion rather than hidden hyperparameters.

However, these advantages come at a cost. By the end of this article, readers will have a complete picture: the algorithm’s framework, a working implementation in MQL5 using the standard C_AO framework, objective results from a standardized benchmark, and an understanding of the scenarios in which CHA makes sense to use and those in which it is better to choose something else.

Author: Andrey Dik