Abstract <p>This paper presents a study of an algorithm that implements the evolution of the macroscopic state of an arbitrary system toward reproducing and further preserving the distribution defined on the configuration space of this system. Through methodological analysis and conceptual modifications, we show that this algorithm, originally proposed for physical modeling in thermodynamics and statistical physics, represents a universal mechanism that produces quasi-continuous dynamics of the structured state of a system with controlled directionality. At the same time, all changes, being macroscopic in nature, are determined at the microlevel of single “agents,” whose behavior can be regulated within a heuristic approach. It is demonstrated that this dynamics retains effectiveness and numerical stability in different modes of functional application. In particular, we consider a problem for a complete rearrangement of the system state from one structure to another, a problem to synthesize competing structures within a single state, and a maze-solving problem defined at the level of structural topology. The key advantage of the algorithm is the almost complete independence of its performance and accuracy from both the information volume of the system states and their structural complexity. This allows us to go beyond the known limitations of standard approaches to numerical modelling and provides new opportunities and approaches for digital twinning applied to different types of large-scale, multifactor systems.</p>