Solving Travelling Salesman Problem by Using Optimization Algorithms

Saud, Suhair; Kodaz, Halife; Babaoğlu, İsmail

doi:10.18502/kss.v3i1.1394

Cited by 9 publications

(14 citation statements)

References 12 publications

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“…This study uses ant colony optimization (ACO), similar to the one presented by Dorigo et al (1997Dorigo et al ( , 1999, to develop the route connecting the station locations. The main reason for choosing ACO is its quick convergence and efficiency in solving Hamiltonian path problem (like TSP) over other artificial intelligence-based heuristics algorithms, such as the Genetic Algorithm, Particle Swarm Optimization, and Shuffled Frog Leaping Algorithms (Brucal and Dadios 2017;Saud et al 2018). ACO uses ants' foraging behavior by means of a pheromone trail to find the optimal solution.…”

Section: Solution Methodologymentioning

confidence: 99%

A Station Location Identification Model for an Integrated Interoperable High-Speed Rail System

Roy

Maji

2019

SSRN Journal

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The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Working papers are subject to formal revision and correction before they are finalized and considered published.

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Section: Solution Methodologymentioning

confidence: 99%

A Station Location Identification Model for an Integrated Interoperable High-Speed Rail System

Roy

Maji

2019

SSRN Journal

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“…A firefly moves toward the most attractive firefly based on the brightness and the distance. The attractiveness of firefly-j seen by firefly-i is any monotonic decreasing function in (2),…”

Section: Local Search By Famentioning

confidence: 99%

“…In MmTSP, more than one salesman must visit a set of cities exactly once and depart from different departure cities, called depots, and must return to their respective departure cities [1]. Finding the exact solution of the shortest route is more difficult as the number of visited cities increase [2].…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization

Farisi

Setiyono

Danandjojo

2021

IJ-AI

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<span>This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.</span>

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“…Transporation is needed to guarantee the mobility of people and goods and as part of the economic system, transportation has an important function in national development [3]. The concept of TSP is described as follows: a salesman or a vehicle has a number of cities to visit with a distance of time between two cities and each city is visited only once (Hamiltonian cycle) and returns to the starting city then the total distance or time is minimized [4,5,6]. Distance is a key factor in transportation.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Optimization Methods for Solving Traveling Salesman Problem

Chandra

Natalia

2021

ITSSI

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The subject of this research is distance and time of several city tour problems which known as traveling salesman problem (tsp). The goal is to find out the gaps of distance and time between two types of optimization methods in traveling salesman problem: exact and approximate. Exact method yields optimal solution but spends more time when the number of cities is increasing and approximate method yields near optimal solution even optimal but spends less time than exact methods. The task in this study is to identify and formulate each algorithm for each method, then to run each algorithm with the same input and to get the research output: total distance, and the last to compare both methods: advantage and limitation. Methods used are Brute Force (BF) and Branch and Bound (B&B) algorithms which are categorized as exact methods are compared with Artificial Bee Colony (ABC), Tabu Search (TS) and Simulated Annealing (SA) algorithms which are categorized as approximate methods or known as a heuristics method. These three approximate methods are chosen because they are effective algorithms, easy to implement and provide good solutions for combinatorial optimization problems. Exact and approximate algorithms are tested in several sizes of city tour problems: 6, 9, 10, 16, 17, 25, 42, and 58 cities. 17, 42 and 58 cities are derived from tsplib: a library of sample instances for tsp; and others are taken from big cities in Java (West, Central, East) island. All of the algorithms are run by MATLAB program. The results show that exact method is better in time performance for problem size less than 25 cities and both exact and approximate methods yield optimal solution. For problem sizes that have more than 25 cities, approximate method – Artificial Bee Colony (ABC) yields better time which is approximately 37% less than exact and deviates 0.0197% for distance from exact method. The conclusion is to apply exact method for problem size that is less than 25 cities and approximate method for problem size that is more than 25 cities. The gap of time will be increasing between two methods when sample size becomes larger.

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Solving Travelling Salesman Problem by Using Optimization Algorithms

Cited by 9 publications

References 12 publications

A Station Location Identification Model for an Integrated Interoperable High-Speed Rail System

A Station Location Identification Model for an Integrated Interoperable High-Speed Rail System

A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization

Performance Analysis of Optimization Methods for Solving Traveling Salesman Problem

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