Preliminary communication

https://doi.org/10.31803/tg-20220407095736

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

Luka Olivari orcid.org/0000-0003-2214-1552 ; Polytechnic of Sibenik, Trg Andrije Hebranga 11, 22 000 Šibenik, Croatia
Luca Olivari ; Polytechnic of Sibenik, Trg Andrije Hebranga 11, 22 000 Šibenik, Croatia

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.

Keywords

ACO; Ant Colony Optimization; execution time; programming language; Traveling Salesmen Problem; TSP

Hrčak ID:

276154

URI

https://hrcak.srce.hr/276154

Publication date:

8.5.2022.

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