PENYELESAIAN MULTI TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA GENETIKA

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.

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Model Penentuan Rute Terpendek Penjemputan Sampah Menggunakan Metode MTSP dan Algoritma Genetika

Journal of Applied Computer Science and Technology ◽

10.52158/jacost.v2i1.168 ◽

2021 ◽

Vol 2 (1) ◽

pp. 43-48

Author(s):

Aswandi ◽

Sugiarto Cokrowibowo ◽

Arnita Irianti

Keyword(s):

Genetic Algorithm ◽

Traveling Salesman Problem ◽

Traveling Salesman ◽

Individual Fitness ◽

Problem Method ◽

Multiple Traveling Salesman Problem ◽

Shortest Route ◽

Fitness Value ◽

The Creation ◽

The Traveling Salesman Problem

Garbage pick-ups performed by two or more people must have a route in their pickup. However, it is not easy to model the route of the pickup that each point must be passed and each point is only passed once. Now, the method to create a route has been done a lot, one of the most commonly used methods is the creation of routes using the Traveling Salesman Problem method. Traveling Salesman Problem is a method to determine the route of a series of cities where each city is only traversed once. In this study, the shortest route modeling was conducted using Multiple Traveling Salesman Problem and Genetic Algorithm to find out the shortest route model that can be passed in garbage pickup. In this study, datasets will be used as pick-up points to then be programmed to model the shortest routes that can be traveled. The application of Multiple Traveling Salesman Problem method using Genetic Algorithm shows success to model garbage pickup route based on existing dataset, by setting the parameters of 100 generations and 100 population and 4 salesmen obtained 90% of the best individual opportunities obtained with the best individual fitness value of 0.05209. The test was conducted using BlackBox testing and the results of this test that the functionality on the system is 100% appropriate.

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Developing an Android-Based City Tour App using Evolutionary Algorithm

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v15i14.20275 ◽

2021 ◽

Vol 15 (14) ◽

pp. 193

Author(s):

Abidatul Izzah ◽

Irmala Arin Kusuma ◽

Yudi Irawan ◽

Toga Aldila Cinderatama ◽

Benni Agung Nugroho

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithm ◽

Mobile Devices ◽

Traveling Salesman Problem ◽

Traveling Salesman ◽

Brute Force ◽

Running Time ◽

Force Method ◽

Brute Force Method ◽

The City

Traveling around a city and making transit in certain areas is called a city tour. Furthermore, determining the optimal city tour route can be considered as a traveling salesman problem. There are many kinds of algorithms to solve this, one of which is the Genetic Algorithm (GA). In developing the City Tour application, a platform is needed to be taken to various places anywhere and anytime. Finally, we developed an application that runs on mobile devices. This application is built on the Android platform so that its use can be more efficient. Furthermore, it can be concluded that the GA applied to the Android-based City Tour Application is reliable to determine city tour routes; this is evidenced by comparing GA with the brute force method, where GA provides optimum results with less running time.

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Revisiting Traveling Salesman Problem (TSP): Analysis of GA and SA based Solutions

International Journal of Recent Contributions from Engineering Science & IT (iJES) ◽

10.3991/ijes.v9i2.22317 ◽

2021 ◽

Vol 9 (2) ◽

pp. 44

Author(s):

Darius Bethel ◽

Hakki Erhan Sevil

Keyword(s):

Genetic Algorithm ◽

Traveling Salesman Problem ◽

Optimal Solution ◽

Comprehensive Analysis ◽

Traveling Salesman ◽

Parameter Analysis ◽

Hard Problem ◽

Shortest Route ◽

Original Contribution ◽

Np Hard Problem

The purpose of this study to analyze genetic algorithm (GA) and simulated an-nealing (SA) based approaches applied to well-known Traveling Salesman Prob-lem (TSP). As a NP-Hard problem, the goal of TSP is to find the shortest route possible to travel all the cities, given a set of cities and distances between cities. In order to solve the problem and achieve the optimal solution, all permutations need to be checked, which gets exponentially large as more cities are added. Our aim in this study is to provide comprehensive analysis of TSP solutions based on two methods, GA and SA, in order to find a near optimal solution for TSP. The re-sults of the simulations show that although the SA executed with faster comple-tion times comparing to GA, it took more iterations to find a solution. Additional-ly, GA solutions are significantly more accurate than SA solutions, where GA found a solution in relatively less iterations. The original contribution of this study is that GA based solution as well as SA based solution are developed to perform comprehensive parameter analysis. Further, a quantifiable comparison is provided for the results from each parameter analysis of GA and SA in terms of performance of solving TSP.

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An Application of Genetic Algorithm in Determining Salesmen’s Routes: A Case Study

Jurnal Optimasi Sistem Industri ◽

10.25077/josi.v17.n1.p26-34.2018 ◽

2018 ◽

Vol 17 (1) ◽

pp. 26

Author(s):

Noufal Zhafira ◽

Feri Afrinaldi ◽

Taufik Taufik

Keyword(s):

Genetic Algorithm ◽

Traveling Salesman Problem ◽

Pharmaceutical Products ◽

Traveling Salesman ◽

Future Research ◽

Distribution Problem ◽

Distribution Company ◽

Time Required ◽

The City

This paper presents a case study of determining vehicles’ routes. The case is taken from a pharmaceutical products distribution problem faced by a distribution company located in the city of Padang, Indonesia. The objective of this paper is to reduce the total distribution time required by the salesmen of the company. Since the company uses more than one salesman, then the problem is modeled as a multi traveling salesman problem (m-TSP). The problem is solved by employing genetic algorithm (GA) and a Matlab® based computer program is developed to run the algorithm. It is found that, by employing two salesmen only, the routes produced by GA results in a 30% savings in total distribution time compared to the current routes used by the company (currently the company employs three salesmen). This paper determines distances based on the latitude and longitude of the locations visited by the salesmen. Therefore, the distances calculated in this paper are approximations. It is suggested that actual distances are used for future research.

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