The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

Traveling Salesman ◽

Travelling Salesman ◽

Algorithm Animation ◽

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.

Interdisciplinary Perspectives on Operations Management and Service Evaluation - Advances in Logistics, Operations, and Management Science ◽

Metaheuristics Approaches for the Travelling Salesman Problem on a Spherical Surface

10.4018/978-1-7998-5442-5.ch005 ◽

2021 ◽

pp. 94-113

Author(s):

Yusuf Sahin ◽

Erdal Aydemir ◽

Kenan Karagul ◽

Sezai Tokat ◽

Burhan Oran

Keyword(s):

Genetic Algorithms ◽

Spherical Surface ◽

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.

Multi Integer Linear Programming and the Traveling Salesman Problem

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.34801 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2417-2421

Author(s):

S. Sathyapriya

Keyword(s):

Linear Programming ◽

Integer Linear Programming ◽

Initial Problem ◽

Traveling Salesman ◽

Travelling Salesman ◽

Integer 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.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

GENETIC ALGORITHM FOR OPTIMIZING DISTRIBUTION WITH ROUTE RESTRICTION CONSTRAINT DUE TO TRAFFIC JAMS

10.5194/isprs-archives-xliv-4-w3-2020-295-2020 ◽

2020 ◽

Vol XLIV-4/W3-2020 ◽

pp. 295-301

Author(s):

N. Mouttaki ◽

J. Benhra ◽

G. Rguiga

Keyword(s):

Genetic Algorithm ◽

Combinatorial Optimization ◽

Classical Problem ◽

Traveling Salesman ◽

Travelling Salesman ◽

Simple Method ◽

Traffic Jams ◽

Better Than

Abstract. The Travelling Salesman Problem (TSP) is a classical problem in combinatorial optimization that consists of finding the shortest tour through all cities such that the salesman visits each city only one time and returns to the starting city. Genetic algorithm is one of the powerful ways to solve problems of traveling salesman problem TSP. The current genetic algorithm aims to take in consideration the constraints happening during the execution of genetic algorithm, such as traffic jams when solving TSP. This program has two important contributions. First one is proposing simple method into taking in consideration an inconvenient route linked to traffic jams. The second one is the use of closeness strategy during the initialization step, which can accelerate the execution time of the algorithm.The results of the experiments show that the improved algorithm works better than some other algorithms. The conclusion ends the analysis with recommendations and future works.

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

Eduvest - Journal Of Universal Studies ◽

10.36418/edv.v1i8.144 ◽

2021 ◽

Vol 1 (8) ◽

pp. 752-756

Author(s):

Ifham Azizi Surya Syafiin ◽

Sarah Nur Fatimah ◽

Muchammad Fauzi

Keyword(s):

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.

Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

Evolutionary Computation ◽

10.1162/evco.1993.1.4.313 ◽

1993 ◽

Vol 1 (4) ◽

pp. 313-333 ◽

Cited By ~ 21

Author(s):

Christine L. Valenzuela ◽

Antonia J. Jones

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Traveling Salesman ◽

Divide And Conquer ◽

Search Problem ◽

Genetic Approach ◽

Solution Quality ◽

Alternative Approach ◽

Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.

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

Asian Journal of Advanced Research and Reports ◽

10.9734/ajarr/2020/v12i130281 ◽

2020 ◽

pp. 68-77

Author(s):

Bogdan-Vasile Cioruța ◽

Alexandru Lauran ◽

Mirela Coman

Keyword(s):

Execution Time ◽

Emergent Behavior ◽

Traveling Salesman ◽

Ant Colony Optimisation ◽

Travelling Salesman ◽

Wolfram Mathematica ◽

Computational Program ◽

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.

Genetic algorithm based approach to solve travelling salesman problem with one point crossover operator:

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v10i3.3269 ◽

2013 ◽

Vol 10 (3) ◽

pp. 1393-1400 ◽

Cited By ~ 3

Author(s):

Sharadindu Roy

Keyword(s):

Genetic Algorithm ◽

Global Optimum ◽

Traveling Salesman ◽

Mutation Operator ◽

Crossover Operator ◽

Travelling Salesman ◽

Apply Genetic ◽

Multiple Copies ◽

In this paper, the travelling salesman problem using genetic algorithm has been attempted. In this practical paper solution is easy and we can easily apply genetic operator in this type of problem. Complexity is both in time and space, provided size of the problem an as integer (count is infinite). The solution of the traveling salesman problem is global optimum. There are cities and given distances between them. Traveling salesman has to visit all of them. TSP main objective is to find traveling sequence of cities to minimize the traveling distance.* traverse one time*initially we select parent1 & parent2 by Roulette wheel concept. Apply one point crossover operator on parents and produce the offspring. Again we apply the mutation operator on offspring and created child. But the no. of bits (cities) will be inverted by the mutation operator, that is depended on mutation probability (pm). So one generation contain 6 individual. Then count fitness of the individuals in each generation. For the next generation (for parent1 & parent2) two individuals will be selected whose fitness is best in generation. Here we see crossover between two good solution may not always yield a better or as good a solution. Since parents are good, so the probability of the child being good is high. Every time we have to do, identity the good solution in the population and make multiple copies of the good solution.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

Computational Intelligence and Neuroscience ◽

10.1155/2017/7430125 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 26

Author(s):

Abid Hussain ◽

Yousaf Shad Muhammad ◽

M. Nauman Sajid ◽

Ijaz Hussain ◽

Alaa Mohamd Shoukry ◽

...

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Traveling Salesman ◽

Crossover Operator ◽

Mutation Operators ◽

Path Representation ◽

Hard Problems ◽

The Traveling Salesman Problem ◽

Survival Of The Fittest

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

A COMPARATIVE STUDY OF CROSSOVER OPERATORS FOR GENETIC ALGORITHMS TO SOLVE TRAVELLING SALESMAN PROBLEM

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v5.i2.2017.1740 ◽

2017 ◽

Vol 5 (2) ◽

pp. 284-291

Author(s):

Wafaa Mustafa Hameed ◽

Asan Baker Kanbar

Keyword(s):

Genetic Algorithms ◽

Comparative Study ◽

Traveling Salesman ◽

Crossover Operator ◽

Natural Evolution ◽

Different Types ◽

Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to ﬁnd good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used , one of the problems in using genetic algorithms is the choice of crossover operator Many crossover operators have been proposed in literature on evolutionary algorithms, however, it is still unclear which crossover operator works best for a given optimization problem. This paper aims at studying the behavior of different types of crossover operators in the performance of genetic algorithm. These types of crossover are implemented on Traveling Salesman Problem (TSP); Whitley used the order crossover (OX) depending on specific parameters to solve the traveling salesman problem, the aim of this paper is to make a comparative study between order crossover (OX) and other types of crossover using the same parameters which was Whitley used.

Using genetic algorithm to solve multiple traveling salesman problem and considering Carbon emissions

Indian Journal of Science and Technology ◽

10.17485/ijst/v13i36.1316 ◽

2020 ◽

Vol 13 (36) ◽

pp. 3707-3715

Author(s):

Chris Jojo Obi ◽

Keyword(s):

Genetic Algorithm ◽

Carbon Emission ◽

Multiple Travelling Salesman Problem

Time Windows ◽

Traveling Salesman ◽

Transport Cost ◽

Travelling Salesman ◽

Insertion Method ◽

Objectives: The Multiple Travelling Salesman problem is a complex combinatorial optimization problem which is a variance of the Traveling Salesman Problem,where a lot of salesmen are utilized in the solution. In this work a cold chain logistics and route optimization model with minimum transport cost, carbon cost and Refrigeration cost are constructed. Methods: A genetic algorithm is then proposed to solve for the Multiple Travelling Salesman Problem with time windows while transport cost, carbon emission cost and refrigeration cost is minimized. Findings: It was observed that the algorithm evolved towards the direction of the optimal value of the fitness function. Novelty: There are a number of studies that considered tournament selection strategy but just a few have applied genetic algorithm considering insertion method to solve a Multiple Travelling salesman Problem. This study uses insertion method to obtain optimal solution. Also, the researcher considered time windows, transport cost, carbon emission cost and refrigeration cost. Keywords: Genetic algorithm method; cold-logistics; multiple travelling salesman problem