Using metaheuristic algorithms to solve the Traveling Salesman Problem

Abstract

The article considers and analyzes the existing metaheuristic algorithms. The possibility of their use in the construction and transfer of data within the computer system. Also their application in local or global networks of united computers. The principles that formed the basis of the formation of the concept of algorithm in the field of information technologies are mentioned. The relevance of using metaheuristic algorithms in the design of data transmission networks is described. The content of the concept of algorithm and its complexity, used in the context of this article, are established. The metaheuristic algorithm and its differences from others are defined. The peculiarities of specific types of these algorithms for finding the optimal path are considered. Concepts of the particle swarm method, which use collective intelligence to implement gravitational search, are defined as the basis of this approach. Some types of metaheuristic algorithms are considered, in particular, ant and bee algorithms. The gravitational search method, which is the basis of the previously mentioned heuristic optimization algorithms, is described. The definition of collective intelligence for this context was established, which formed the basis of this method of solving the traveling salesman's task. The relevance of the use of specific, previously mentioned, algorithms is described. Demonstration of systems in which the use of metaheuristic algorithms is possible. The possibility of their applied application in applied spheres of human and social activity is indicated. Methods of effective application of ant and bee algorithms are proposed. Advantages and disadvantages of their use are highlighted. Possibilities of further modernization of these specific methods of solving the traveling salesman's task are formulated. Advantages of bee and ant algorithms over genetic algorithms are described. Highlighting the advantages of this property for the practical application of these algorithms for solving optimization problems