Firefly algorithm and ant colony algorithm to optimize the traveling salesman problem

Through the study of ACOTSP, it is found that the previous ant colony algorithm will fall into local optimal when increasing pheromone concentration factor. In order to solve the problem, we through the improved pheromone concentration factor to view your traveling salesman solving process, through...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-04, Vol.2253 (1), p.12010
Main Authors: Yu, XiaoFei, Yu, LinWen, Zheng, MingQiao, Lu, JunHui, Zhang, Lü
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Design optimization
Heuristic methods
Pheromones
Physics
Traveling salesman problem
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Summary:Through the study of ACOTSP, it is found that the previous ant colony algorithm will fall into local optimal when increasing pheromone concentration factor. In order to solve the problem, we through the improved pheromone concentration factor to view your traveling salesman solving process, through the simulation experiments found that due to the pheromone concentration gradually increased with the number of iterations, pheromone concentration and pheromone concentration factor exponential relationship, lead to appear even if the distance is large move also can appear probability is very high. In this design, the ant colony algorithm is optimized by introducing firefly algorithm (FA): the ant colony movement deviation is avoided by adding disturbance factor; and the migration probability caused by excessive pheromone concentration is solved by adding function relation between moving distance and pheromone concentration. Simulation results show that the optimized algorithm has better results and is not easy to fall into local optimum.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2253/1/012010