An improved ant colony algorithm for TSP application

Autor:	Che Liu, Zerui Li, Deqiang Jiao, Dinghao Wang
Rok vydání:	2021
Předmět:	History Computer science business.industry Ant colony optimization algorithms MathematicsofComputing_NUMERICALANALYSIS Artificial intelligence business ComputingMethodologies_ARTIFICIALINTELLIGENCE Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1802:032067
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1802/3/032067
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b51d76999d4cca6c2bb63c04971b0f4f https://doi.org/10.1088/1742-6596/1802/3/032067 Zobrazit plný text záznamu