scholarly journals Penerapan Bat Algorithm Dalam Penyelsaian Kasus Travelling Salesman Problem (TSP) Pada Internship Program

2021 ◽  
Vol 6 (2) ◽  
pp. 111-116
Author(s):  
Veri Julianto ◽  
Hendrik Setyo Utomo ◽  
Muhammad Rusyadi Arrahimi
Keyword(s):  
Global Minimum ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Bat Algorithm ◽  
Travelling Salesman ◽  
Street Vendors ◽  
Shortest Distance ◽  
Using Data ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

This optimization is an optimization case that organizes all possible and feasible solutions in discrete form. One form of combinatorial optimization that can be used as material in testing a method is the Traveling Salesman Problem (TSP). In this study, the bat algorithm will be used to find the optimum value in TSP. Utilization of the Metaheuristic Algorithm through the concept of the Bat Algorithm is able to provide optimal results in searching for the shortest distance in the case of TSP. Based on trials conducted using data on the location of student street vendors, the Bat Algortima is able to obtain the global minimum or the shortest distance when compared to the nearest neighbor method, Hungarian method, branch and bound method.

The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2013-2016 ◽  
Author(s):  
Guo Zhi Wen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Algorithm Animation ◽  
The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

scholarly journals A Genetic Method using Hybrid Crossover for Solving Travelling Salesman Problem

2019 ◽  
Vol 8 (2) ◽  
pp. 5066-5072
Keyword(s):  
Hybrid Method ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Performance Comparison ◽  
Initial Population ◽  
Genetic Approach ◽  
Comparison Standard ◽  
Travelling Salesman ◽  
Genetic Method ◽  
Better Than

This paper proposes a Genetic approach using Hybrid Crossover for Solving the Travelling Salesman Problem. Proposed hybrid method generates an initial population using Nearest Neighbor (NN) approach which is modified using “Sub-Path Mutation” (SPM) process. Modified population undergoes Distance Preserving Crossover (DPX) [2] and 2-opt Optimal mutation (2-opt) [1] to check for possible refinement. SPM searches position for the minimum distant city within a given path. This work is motivated by the algorithm developed by [3] who performed DPX and 2-opt mutation on the initial population generated using NN. For performance comparison, standard TSPLIB data is taken. The proposed hybrid method performances better in terms of % best error. It performs better than methods reported in [3 - 11].

scholarly journals The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

2019 ◽  
Vol 12 (2) ◽  
pp. 98-105
Author(s):  
Ajchara Phu-ang ◽  
Duangjai Jitkongchuen
Keyword(s):  
Local Search ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Travelling Salesman Problem ◽  
Hill Climbing ◽  
Search Process ◽  
Initial Population ◽  
Travelling Salesman ◽  
Average Percentage ◽  
Surrounding Areas

This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and  embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.

scholarly journals Multi Integer Linear Programming and the Traveling Salesman Problem

2021 ◽  
Vol 9 (VI) ◽  
pp. 2417-2421
Author(s):  
S. Sathyapriya
Keyword(s):  
Linear Programming ◽  
Traveling Salesman Problem ◽  
Integer Linear Programming ◽  
Travelling Salesman Problem ◽  
Initial Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Integer Problem ◽  
The Traveling Salesman Problem

The Travelling Salesman problem is considered as a binary integer problem. For this problem, several stop variables and subtours are discussed. The stops are generated and the distance between those stops are found, consequently the graphs are drawn. Further the variables are declared and the constraints are framed. Then the initial problem is visualised along with the subtour constraints in order to achieve the required output.

scholarly journals Travelling Salesman Problem Analysis with Complete Enumeration Method, Branch & Bound and Greedy Heuristic

2021 ◽  
Vol 1 (8) ◽  
pp. 752-756
Author(s):  
Ifham Azizi Surya Syafiin ◽  
Sarah Nur Fatimah ◽  
Muchammad Fauzi
Keyword(s):  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Greedy Heuristic ◽  
Problem Analysis ◽  
Heuristic Methods ◽  
Travelling Salesman ◽  
Complete Enumeration ◽  
The Traveling Salesman Problem ◽  
The City

PT XYZ as the best and largest Bed Sheet Set company in Indonesia with products such as Bed Covers, Bed Sheets, Pillowcases, Bolsters and Blankets. The Traveling Salesman Problem (TSP) is a problem faced in finding the best route to visit shops that sell products from PT BIG. A visit to the shop is carried out on the condition that each city can only be visited once except the city of origin. The algorithms applied in this TSP problem include the Complete Enumeration, Branch & Bound and Greedy Heuristic methods.

Metaheuristics Approaches for the Travelling Salesman Problem on a Spherical Surface

2021 ◽  
pp. 94-113
Author(s):  
Yusuf Sahin ◽  
Erdal Aydemir ◽  
Kenan Karagul ◽  
Sezai Tokat ◽  
Burhan Oran
Keyword(s):  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Spherical Surface ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Heuristic Methods ◽  
Approximate Algorithms ◽  
Travelling Salesman ◽  
The Traveling Salesman Problem ◽  
Special Case

Traveling salesman problem in which all the vertices are assumed to be on a spherical surface is a special case of the conventional travelling salesman problem. There are exact and approximate algorithms for the travelling salesman problem. As the solution time is a performance parameter in most real-time applications, approximate algorithms always have an important area of research for both researchers and engineers. In this chapter, approximate algorithms based on heuristic methods are considered for the travelling salesman problem on the sphere. Firstly, 28 test instances were newly generated on the unit sphere. Then, using various heuristic methods such as genetic algorithms, ant colony optimization, and fluid genetic algorithms, the initial solutions for solving test instances of the traveling salesman problem are obtained in Matlab®. Then, the initial heuristic solutions are used as input for the 2-opt algorithm. The performances and time complexities of the applied methods are analyzed as a conclusion.

scholarly journals Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 340
Author(s):  
Gianpaolo Ghiani ◽  
Tommaso Adamo ◽  
Pierpaolo Greco ◽  
Emanuela Guerriero
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Computing Time ◽  
Time Dependent ◽  
Machine Learning Techniques ◽  
Travelling Salesman ◽  
Distribution Management ◽  
Learning Techniques ◽  
Average Improvement

In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5% for London, and about 4% for Paris.

scholarly journals Improved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem

2018 ◽  
Vol 12 (3) ◽  
pp. 1054
Author(s):  
Fayçal Chebihi ◽  
Mohammed essaid Riffi ◽  
Amine Agharghor ◽  
Soukaina Cherif Bourki Semlali ◽  
Abdelfattah Haily
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Travelling Salesman Problem ◽  
Search Method ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization ◽  
The Traveling Salesman Problem

<p>This paper proposes a novel discrete bio-inspired chicken swarm optimization algorithm (CSO) to solve the problem of the traveling salesman problem (TSP) which is one of the most known problems used to evaluate the performance of the new metaheuristics. This problem is solved by applying a local search method 2-opt in order to improve the quality of the solutions. The DCSO as a swarm system of the algorithm increases the level of diversification, in the same way the hierarchical order of the chicken swarm and the behaviors of chickens increase the level of intensification. In this contribution, we redefined the basic different operators and operations of the CSO algorithm. The performance of the algorithm is tested on a symmetric TSP benchmark dataset from TSPLIB library. Therefore, the algorithm provides good results in terms of both optimization accuracy and robustness comparing to other metaheuristics.</p>

scholarly journals Some Analytical Considerations Regarding the Traveling Salesman Problem Solved with Wolfram Mathematica Applications

2020 ◽  
pp. 68-77
Author(s):  
Bogdan-Vasile Cioruța ◽  
Alexandru Lauran ◽  
Mirela Coman
Keyword(s):  
Traveling Salesman Problem ◽  
Execution Time ◽  
Travelling Salesman Problem ◽  
Emergent Behavior ◽  
Traveling Salesman ◽  
Ant Colony Optimisation ◽  
Travelling Salesman ◽  
Wolfram Mathematica ◽  
Computational Program ◽  
The Traveling Salesman Problem

The paper presents an introduction to the Ant Colony Optimisation (ACO) algorithm and methods for solving the Travelling Salesman Problem (TSP). Documenting, understanding and knowledge of concepts regarding the emergent behavior and intelligence swarms optimization, easily led on solving the Travelling Salesman Problem using a computational program, such as Mathematics Wolfram via Creative Demostration Projects (*.cdf) module. The proposed application runs for a different number of ants, a different number of ants, a different number of leaders (elite ants), and a different pheromone evaporation index. As a result it can be stated that the execution time of the algorithm to solve the TSP is direct and strictly proportional to the number of ants, cities and elite ants considered, the increase of the execution time increasing significantly with the increase of the variables.

Sign in / Sign up

Export Citation Format

Share Document