Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
