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Aplikace mravenčích algoritmů
 Olszar, Patrik ; Sedlák, David (referee) ; Bidlo, Michal (advisor)
This bachelor’s thesis focuses on the implementation and optimization of the ant colony algorithm in C++ for solving the traveling salesman problem (TSP) involving tens of thousands to hundreds of thousands of cities. Due to the high memory demands of traditional ant colony algorithms, which have a exponential expansion of the pheromone matrix, a restricted pheromone matrix was implemented. This technique effectively limits the memory size needed for the pheromone matrix and thus enhances the scalability of the algorithm. Additionally, the work uses techniques such as MAX–MIN, ant parallelization, dynamic adjustment of alpha and beta parameters, a nearest neighbor list, and VCSS. The results achieved a final path that is within 3.5-5% of the optimal solution.
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