Open AccessAn improved ant colony algorithm for TSP application
Author(s) -
Deqiang Jiao,
Che Liu,
Zerui Li,
Dinghao Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/3/032067
Subject(s) - ant colony optimization algorithms , travelling salesman problem , crossover , mathematical optimization , selection (genetic algorithm) , genetic algorithm , algorithm , convergence (economics) , meta optimization , computer science , ant colony , mutation , population , parallel metaheuristic , mathematics , artificial intelligence , biochemistry , chemistry , demography , sociology , economics , gene , economic growth
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.