scholarly journals IMPLEMENTASI ALGORITMA GENETIKA DALAM PENENTUAN RUTE TERBAIK PENDISTRIBUSIAN BBM PADA PT BURUNG LAUT

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Test System ◽  
Ant Colony ◽  
Fuel Oil ◽  
Ant Colony System ◽  
Travelling Salesman ◽  
The People ◽  
System Method ◽  
Genetic Algorithm Method

ABSTRACT<br />Fuel Oil <br />(<br />BBM<br />)<br />is one of the important commodities for the people of Indonesia. BBM is<br />distributed by sea. One of the companies whose fleets are working in the distribution of fuel is PT<br />Burung Laut, which is by operating the Tanker MT. Citra Bintang. This ship distributes fuel to the<br />Maluku and Papua areas. But in its distribution, this ship does not have a definite route. Previous<br />research has been done by Closeary et al. By using Ant Colony System method. In this research,<br />conducted the shortest distance search that is passed by ship with Genetic Algorithm method for<br />case study of Traveling Salesman Problem. From the test system that has been done as much as<br />10 times the shortest route produced with a distance of 4853 kilometers. The route of the ship<br />with the distance is Tobelo, Fak - Fak, Kaimana, Tual, Dobo, Merauke, Saumlaki, Namlea,<br />Ambon, Masohi, Sanana, Labuha, and then Ternate<br />Keywords:<br />Genetic Algorithm, Travelling Salesman Problem<br />ABSTRAK<br />Bahan Bakar Minyak <br />(<br />BBM<br />)<br />adalah salah satu komoditas penting bagi masyarakat Indonesia.<br />BBM didistribusikan melalui jalur laut. Salah satu perusahaan yang armada laut yang bekerja dalam<br />pendistribusian BBM adalah PT Burung Laut, yaitu dengan mengoperasikan Kapal Tanker MT.<br />Citra Bintang. Kapal ini mendistribusikan BBM pada daerah Maluku dan Papua. Namun dalam<br />pendistribusiannya, kapal ini tidak memiliki rute yang pasti. Penelitian sebelumnya sudah pernah<br />dilakukan oleh Tutupary, et al. Dengan menggunakan metode Ant Colony System. Pada penelitian<br />ini, dilakukan pencarian jarak terpendek yang dilewati kapal dengan metode Algoritma Genetika<br />untuk studi kasus Travelling Salesman Problem. Dari pengujian sistem yang telah dilakukan<br />sebanyak 10 kali dihasilkan rute terpendek dengan jarak 4.853 kilometer. Adapun rute yang dilalui<br />kapal dengan jarak tersebut adalah Tobelo, Fak-Fak, Kaimana, Tual, Dobo, Merauke, Saumlaki,<br />Namlea, Ambon, Masohi, Sanana, Labuha, dan kemudian terakhir Ternate.<br />Kata Kunci: Algoritma Genetika, Kasus Pedagang Keliling

scholarly journals Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

2019 ◽  
Vol 8 (2S4) ◽  
pp. 91-95
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Soft Computing Techniques

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.

Solution of Travelling Salesman Problem Using Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1206-1212
Author(s):  
Sevda Dayıoğlu Gülcü ◽  
Şaban Gülcü ◽  
Humar Kahramanli
Keyword(s):  
Discrete Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Travelling Salesman ◽  
Ant Colonies ◽  
Discrete Optimization Problems

Recently some studies have been revealed by inspiring from animals which live as colonies in the nature. Ant Colony System is one of these studies. This system is a meta-heuristic method which has been developed based upon food searching characteristics of the ant colonies. Ant Colony System is applied in a lot of discrete optimization problems such as travelling salesman problem. In this study solving the travelling salesman problem using ant colony system is aimed.

Comparative Analysis of ACO Algorithms for the Solution of the Travelling Salesman Problem

2021 ◽  
pp. 338-358
Author(s):  
Gloria Lola Quispe ◽  
Maria Fernanda Rodríguez ◽  
José Daniel Ontiveros
Keyword(s):  
Comparative Analysis ◽  
Travelling Salesman Problem ◽  
Point Of View ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant System ◽  
Travelling Salesman ◽  
Deterministic Algorithms ◽  
Test Instance ◽  
High Degree

Metaheuristics are non-deterministic algorithms. Metaheuristic strategies are related to design. This chapter presents an introduction on metaheuristics, from the point of view of its theoretical study and the foundations for its use. Likewise, a description and comparative study of the ant colony-based algorithms is carried out. These are ant system (AS), ant colony system (ACS), and max-min ant system (MMAS). These results serve to deliver solutions to complex problems and generally with a high degree of combinatorics for those there is no way to find the best reasonable time. An experimentation and analysis of the results of the ACO algorithms (optimization by ants colonies) is also carried out. For the evaluation of the algorithms, comparisons are made for instances of the TSPLIB test instance library. Therefore, it is deepened in the resolution of the travelling salesman problem (TSP), and a comparative analysis of the different algorithms is carried out in order to see which one adjusts better.

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