scholarly journals Ant Colony System with Heuristic Function for the Travelling Salesman Problem

2013 ◽  
Vol 4 (2) ◽  
pp. 39-48
Author(s):  
Mustafa Muwafak Alobaedy ◽  
Ku Ruhana KuMahamud
Keyword(s):  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Ant Colony System ◽  
Heuristic Function ◽  
Travelling Salesman

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.

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 Ants Colony Optimisation of a Measuring Path of Prismatic Parts on a CMM

2016 ◽  
Vol 23 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Slavenko M. Stojadinovic ◽  
Vidosav D. Majstorovic ◽  
Numan M. Durakbasa ◽  
Tatjana V. Sibalija
Keyword(s):  
Travelling Salesman Problem ◽  
Ant Colony ◽  
The Other ◽  
Ant Colony Optimisation ◽  
Travelling Salesman ◽  
Prismatic Parts ◽  
On Line ◽  
Optimisation Model ◽  
Set Of Points ◽  
The Mathematical Model

AbstractThis paper presents optimisation of a measuring probe path in inspecting the prismatic parts on a CMM. The optimisation model is based on: (i) the mathematical model that establishes an initial collision-free path presented by a set of points, and (ii) the solution of Travelling Salesman Problem (TSP) obtained with Ant Colony Optimisation (ACO). In order to solve TSP, an ACO algorithm that aims to find the shortest path of ant colony movement (i.e. the optimised path) is applied. Then, the optimised path is compared with the measuring path obtained with online programming on CMM ZEISS UMM500 and with the measuring path obtained in the CMM inspection module of Pro/ENGINEER®software. The results of comparing the optimised path with the other two generated paths show that the optimised path is at least 20% shorter than the path obtained by on-line programming on CMM ZEISS UMM500, and at least 10% shorter than the path obtained by using the CMM module in Pro/ENGINEER®.

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