Rationale. Solving combinatorial optimization problems by means of heuristic optimization algorithms is a highly important area of computational intelligence as well as artificial intelligence. Methodology. This paper presents empirical (computational) results obtained through experiments with a descent-ascent (D-A) principle-based heuristic optimization algorithm, primarily designed for solving combinatorial optimization problems. The descent-ascent algorithm—referred to briefly in this way—has its origins in the local search paradigm. A distinctive feature is that the minimization of the objective function of an optimization problem (i.e., descent) is combined with certain perturbations of solutions (i.e., ascents) to avoid a greedy/deterministic search behavior and, at the same time, premature convergence to suboptimal local optima. Results. The experiments conducted with this algorithm and the results obtained demonstrate a relatively high level of algorithmic efficiency in solving the well-known combinatorial problem—the quadratic assignment problem. Practical relevance of the research. The problem is well relevant in such areas as the the green economy, next-generation industry, digital transformation, renewable energy, sustainable logistics systems, and other socio-technical and information management contexts.
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