An improved ant colony algorithm for TSP application

Aiming at the problems of slow convergence speed and easy to fall into the optimal solution of ant colony algorithm, genetic algorithm and nonlinear optimization are used to optimize ant colony algorithm. After the initial iteration of the ant colony, the solution formed by all paths is the initial...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1802; H. 3; S. 32067
Hauptverfasser: Jiao, Deqiang, Liu, Che, Li, Zerui, Wang, Dinghao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.03.2021
Schlagworte:
Ant colony optimization
Convergence
Crossovers
Genetic algorithms
Physics
Traveling salesman problem
ISSN:1742-6588, 1742-6596
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Zusammenfassung:Aiming at the problems of slow convergence speed and easy to fall into the optimal solution of ant colony algorithm, genetic algorithm and nonlinear optimization are used to optimize ant colony algorithm. After the initial iteration of the ant colony, the solution formed by all paths is the initial population, and then the genetic algorithm is used for selection, crossover and mutation to improve the ability of global search. Finally, the nonlinear optimization algorithm is used to increase the ability of local search of the algorithm. Through this improvement, the convergence speed of the ant colony algorithm is improved and the problem of easy to fall into the optimal solution is solved, which is applied to the traveling salesman problem.
Bibliographie:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1802/3/032067