PENYELESAIAN MULTI TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA GENETIKA

• NI KADEK MAYULIANA Faculty of Mathematics and Natural Sciences, Udayana University
• EKA N. KENCANA Faculty of Mathematics and Natural Sciences, Udayana University
• LUH PUTU IDA HARINI Faculty of Mathematics and Natural Sciences, Udayana University

Abstract

Genetic algorithm is a part of heuristic algorithm which can be applied to solve various computational problems. This work is directed to study the performance of the genetic algorithm (GA) to solve Multi Traveling Salesmen Problem (multi-TSP). GA is simulated to determine the shortest route for 5 to 10 salesmen who travelled 10 to 30 cities. The performance of this algorithm is studied based on the minimum distance and the processing time required for 10 repetitions for each of cities-salesmen combination. The result showed that the minimum distance and the processing time of the GA increase consistently whenever the number of cities to visit increase. In addition, different number of sales who visited certain number of cities proved significantly affect the running time of GA, but did not prove significantly affect the minimum distance.

Author Biographies

NI KADEK MAYULIANA, Faculty of Mathematics and Natural Sciences, Udayana University

Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

EKA N. KENCANA, Faculty of Mathematics and Natural Sciences, Udayana University

Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

LUH PUTU IDA HARINI, Faculty of Mathematics and Natural Sciences, Udayana University

Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Published
2017-01-20
How to Cite
MAYULIANA, NI KADEK; KENCANA, EKA N.; HARINI, LUH PUTU IDA. PENYELESAIAN MULTI TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA GENETIKA. E-Jurnal Matematika, [S.l.], v. 6, n. 1, p. 1-6, jan. 2017. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/27163>. Date accessed: 07 june 2020. doi: https://doi.org/10.24843/MTK.2017.v06.i01.p141.
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Keywords

Genetic Algorithm; Multi Traveling Salesman Problem; simulation