METHODOLOGY OF USING BIOINSPIRED METHODS FOR INTEGRATED PROCESSING OF GREAT DATA ON THE EXAMPLE OF THE TRANSPORT TYPE PROBLEM SOLUTION
This paper presents a methodology for using bioinspired methods for integrated processing of big data using the example of solving the transport type problem. The main place among the applied problems of the transport type is given to the tasks of building transport facilities, which make it possible to minimize vehicle traffic problems or minimize the cost of transportation, minimize the cost of transport problems. Routing is the most perfect way to organize the flow of goods from enterprises, which has a significant impact on the acceleration of traffic turnover with rational and efficient use of it. For this class of combinatorial problems, there are no effective classical methods and algorithms for solving. These tasks are characterized by a finite, but very large number of possible solutions. They can be put as tasks of integer programming, but in this case there are no efficient algorithms. Therefore, the development of methods and algorithms for solving transport-type problems, which has been carried out for many years, is still an urgent problem. The methodological analysis of the research problem has been carried out. The analysis showed that the use of methods and algorithms of sequential and parallel bio-inspired search for solving the considered class of transport-type problems is an actual scientific problem of practical interest. The problem statement is given. An integrated search scheme is shown, which allows you to parallelize the process of finding an acceptable solution for large-scale problems. The structural scheme of the bio-inspired search for the problem of the extreme path is considered. The results of computational experiments are presented. The research results allow us to conclude that the time complexity of the considered bioinspired search algorithms does not exceed the limits of the polynomial dependence, and can be expressed by the formula: O (N2), where N is the number of graph vertices (size of the problem to be solved).