Discussing the article: "Archimedes Optimization Algorithm (AOA)"

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Check out the new article: Archimedes Optimization Algorithm (AOA).

The article examines the Archimedes Optimization Algorithm — a metaheuristic in which an agent is represented as a physical object with density, volume, and acceleration, and the search itself is reinterpreted as the tendency of bodies submerged in a fluid to reach equilibrium. The balance between exploration and exploitation is not dictated by an external schedule here, but rather stems from the physics of decaying turbulence. We will implement the algorithm in MQL5, test it on a standard test bench, and analyze where this approach works.

We are used to thinking of optimization as a climb: there is a landscape, there is a peak, and the goal is to reach it. AOA does not climb. It relaxes toward equilibrium. The term "optimum" is redefined here — it is not the highest point, but rather the state in which the system has come to rest. And in this model, the agent is not the usual bare coordinate vector, as in almost all swarm methods, but a physical object with density, volume, and acceleration—and each of these properties has its own dynamics.

This approach provides, for free, what other algorithms achieve by building separate mechanisms. Drop a handful of objects into choppy water — at first, they collide with one another in a chaotic manner. That is exploration: a broad, undirected survey of the search space. The turbulence subsides — the objects stop colliding and begin to drift smoothly toward their equilibrium positions. That is exploitation: carefully refining the solutions already found. In AOA, the transition from the first mode to the second is not determined by a decaying coefficient superimposed on the algorithm — it occurs naturally as the turbulence subsides. The exploration/exploitation balance becomes an intrinsic property of the model, rather than an external add-on that must be fine-tuned manually.

Author: Andrey Dik