Influence of Programming Language on the Execution Time of Ant Colony Optimization Algorithm

Abstract

Supply chains can be accelerated by route optimization, a computationally intensive process for a large number of instances. Traveling Salesmen Problem, as the representative example of routing problems, is NP-hard combinatorial problem. It means that the time needed for solving the problem with exact methods increases exponentially with the increased dataset. Using metaheuristic methods, like Ant Colony Optimization, reduces the time needed for solving the problem drastically but finding a solution still takes a considerable amount of time for large datasets. In today's dynamic environment finding the solution as fast as possible is important as finding a quality solution. The programming language used for finding the solution also influences execution time. In this paper, the execution time of Ant Colony Optimization to solve Traveling Salesman Problems of different sizes was measured. The algorithm was programmed in several programming languages, execution time was measured to rank programming languages.