APPLICATION OF ANT OPTIMIZATION ALGORITHMS IN THE SOLUTION OF THE ROUTING PROBLEM

Abstract

The article discusses current issues of using evolutionary algorithms to solve the routing problem. Ant algorithms (MA), like most types of evolutionary algorithms, are based on the use of a population of potential solutions and are designed to solve combinatorial optimization problems, first of all, search for various paths on graphs. The cooperation between individuals (artificial ants) is implemented on the basis of stigmetry modeling. In addition, each agent, called artificial ant, is looking for a solution to the problem. Artificial ants consistently build a solution to the problem, moving around the graph, lay the pheromone and, when choosing a further section of the path, take into account the concentration of this enzyme. The higher the concentration of pheromone in the subsequent section, the greater the likelihood of its choice. Since MA is based on the movement of ants along some paths, MAs are effective, first of all, in solving problems that can be interpreted in the form of a graph. Computer experiments showed that the efficiency of MA increases with increasing dimension of the problem and for tasks on high-dimensional graphs they work faster than other evolutionary algorithms. Good results were also noted in solving non-stationary problems on graphs with a changing environment. In connection with this, the implementation of the meta - heuristic method is proposed as a modification of ant optimization algorithms. The scheme of the system is presented. A software product specification is also provided