ALGORITHM FOR CONSTRUCTING THE ROUTE OF A ROBOTIC COMPLEX USING THE FUZZY LOGIC METHOD

This study is devoted to the analysis and optimization of the ant colony algorithm for solving thetraveling salesman problem, a classic NP-hard combinatorial optimization problem. The primary objectiveof the work is to experimentally assess the impact of the algorithm’s parameters on the quality andefficiency of the search for approximate solutions, as well as to develop recommendations for their adaptivetuning. The standard Berlin52 graph from the TSPLIB library—containing the coordinates of 52 citieswith a known optimal route length of 7542 units—was used as the test dataset. Experiments were conductedin a Python environment using the ACO-Pants library, which implements the ant colony algorithm.A series of 10 runs with fixed parameters was performed: number of ants (20), number of iterations (100),pheromone influence coefficient (α = 1.0), distance coefficient (β = 2.0), and pheromone evaporation rate(ρ = 0.5). The results showed an average deviation from the optimum of 1.85%, with the best found solutionbeing 7675.23 (a deviation of 1.67%). To enhance the algorithm’s efficiency, adaptive mechanismsfor dynamic parameter tuning were explored: a linear increase of α (up to 2.0) and a decrease of β (to3.0), a reduction of ρ (to 0.3), as well as an increase in the number of ants (up to 30). These modificationsreduced the average deviation to 1.70% and improved the stability of the solutions. Particular attentionwas paid to analyzing the balance between exploring new routes and exploiting accumulated data. It wasfound that increasing the number of ants improves the quality of solutions; however, beyond 30 agents, theefficiency gains diminish. Dynamic adjustment of the parameters prevents premature convergence to localminima and accelerates the search for globally optimal paths. Visualization of the convergence dynamicsconfirmed a rapid decrease in route length during the first 20 iterations, followed by subsequent stabilization.The practical significance of this work lies in demonstrating the flexibility of the ant colony algorithmfor routing tasks in logistics and network planning. The results indicate that ACO outperforms generalpurposemethods (for example, genetic algorithms) in computational efficiency for the TSP. The developedrecommendations for parameter tuning can be applied to scale the algorithm to larger graphs. Overall,the study emphasizes the importance of adaptive approaches in metaheuristic optimization and opens upprospects for further improvements through hybridization with other methods.