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Ant colony system algorithms, as a new class of global search algorithms, can solve the TSP (Traveling Salesman Problem). At the same time, the TSP problem is a classical NP-C problem, the solution of TSP problem involves many fields, such as network routing, vehicle routing, logistics and transportation, so it is very important to solve TSP problem effectively. In this experiment, the ant colony optimization algorithm is simulated and experimented using Matlab software on bayg29 dataset and the algorithm strategy is improved by controlling different parameters. Our main goal is to improve the performance of the algorithm through meticulous parameter fine-tuning and policy improvement. We explored a range of parameter adjustments, including variables such as pheromone evaporation rate, ant colony size, etc. By undertaking this comprehensive investigation, we aspire to push the boundaries of ant colony optimization, unlocking its potential to deliver highly efficient solutions for complex TSP instances. This research holds promise for revolutionizing the efficiency and cost-effectiveness of solutions across a wide range of practical applications.
Zhengyang Shen (Mon,) studied this question.
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