Parallel implementation of the ant colony algorithm with parameters update using the genetic algorithm

The paper considers the possibility of using a joint implementation of a hybrid method using the ant colony optimization with genetic algorithm for solving traveling salesman problem. It is known that the ant colony optimization is sensitive to its parameters, so the search for the optimal parameters of the ant colony is suitable as a problem, the solution of which is related to the genetic algorithm. The one of the purposes of calculations parallelization is to reduce execution time, but not every algorithm has an effective parallel implementation. It is known that the genetic algorithm and the ant colony optimization are parallelized. The paper studies the possibility of constructing parallel computations for the hybrid method presented. The traveling salesman problem on which the research is conducted is an NP-complete problem and it is often used to test combinatorial optimization algorithms. It is shown that parallelization of the method used leads to an increase in the speed of the algorithm.