Proposed Algorithms to solve Big Data traveling salesman problem

Abstract

Big data is one of the most concerned topics in business today across information technology sectors. most research fields toward using big data tools to leverage from the huge data that available today. The traveling salesman problem is one of problems that is growth by increasing the input as a factorial (n!). therefore it is important to find algorithm to solve big number of cities with feasible time and within available memory space. This article introduces two proposed algorithms to solve traveling salesman problem by clustering using three methods; k-means, Gaussian Mixture Model, and Self-Organizing Map to select the best one for proposed algorithms. The proposed algorithms depend on arranging the cities (points) in chromosomes for Genetic Algorithm after clustering the big data to reduce the problem and solving each cluster separately based on divide and conquer concept. The two proposed algorithms tested by applying on different number of points, the nearest points algorithm solved traveling salesman problem with 2 million points. number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of datasets that qualify as big data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, big data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes)." [3]. 2. Traveling Salesman Problem (TSP)