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.

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.

scholarly journals Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Ant Colony Optimization ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
The Other ◽  
Critical Properties ◽  
Ant System ◽  
Travelling Salesman ◽  
Time Limits ◽  
System Models ◽  
Benchmark Datasets

In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions.

scholarly journals An Early Exploratory Method to Avoid Local Minima in Ant Colony System

1970 ◽  
Vol 10 (1) ◽  
pp. 33-41
Author(s):  
Thanet Satukitchai ◽  
Kietikul Jearanaitanakij
Keyword(s):  
Job Shop ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Search Space ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Ant Colony System ◽  
Ant System ◽  
Local Optima ◽  
Combinatorial Optimization Problems

Ant Colony Optimization (ACO) is a famous technique for solving the Travelling Salesman Problem (TSP.) The first implementation of ACO is Ant System. Itcan be used to solve different combinatorial optimization problems, e.g., TSP, job-shop scheduling, quadratic assignment. However, one of its disadvantages is that it can be easily trapped into local optima. Although there is an attempt by Ant Colony System (ACS) to improve the local optima by introducing local pheromone updating rule, the chance of being trapped into local optima still persists. This paper presents an extension of ACS algorithm by modifying the construction solution phase of the algorithm, the phase that ants move and build their tours, to reduce the duplication of tours produced by ants. This modification forces ants to select unique path which has never been visited by other ants in the current iteration. As a result, the modified ACS can explore more search space than the conventional ACS. The experimental results on five standard benchmarks from TSPLIB show improvements on both the quality and the number of optimal solutions founded.

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

An Improved Ant Colony System Based on Negative Biased

2010 ◽  
Vol 439-440 ◽  
pp. 558-562
Author(s):  
Jin Qiu Yang ◽  
Jian Gang Yang ◽  
Gen Lang Chen
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant System

Ant System (AS) was the first Ant Colony Optimization (ACO) algorithm, which converged too slowly and consumed huge computation. Among the variants of AS, Ant Colony System (ACS) was one of the most successful algorithms. But ACS converged so rapidly that it always was in early stagnation. An improved Ant Colony System based on Negative Biased (NBACS) was introduced in the paper to overcome the early stagnation of the ACS. Experiments for Traveling Salesman Problem (TSP) showed that better solutions were obtained at the same time when the convergence rate accelerated more rapidly.

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