scholarly journals An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Md. Sabir Hossain ◽  
Ahsan Sadee Tanim ◽  
Sadman Sakib Choudhury ◽  
S. M. Afif Ibne Hayat ◽  
Muhammad Nomani Kabir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Minimum Cost ◽  
Variable Order ◽  
Crossover Operator ◽  
Classical Genetic ◽  
Multiple Orders ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem ◽  
Crossover Method

The traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm.

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

2013 ◽  
Vol 10 (3) ◽  
pp. 1393-1400 ◽  
Author(s):  
Sharadindu Roy
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Global Optimum ◽  
Traveling Salesman ◽  
Mutation Operator ◽  
Crossover Operator ◽  
Travelling Salesman ◽  
Apply Genetic ◽  
Multiple Copies ◽  
The Traveling Salesman Problem

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. 

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 Penentuan Rute Pengiriman Ice Tube di Kota Malang dengan Algoritma Genetika

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Nurina Savanti Widya Gotami ◽  
Yane Marita Febrianti ◽  
Robih Dini ◽  
Hamim Fathul Aziz ◽  
San Sayidul Akdam Augusta ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Combinatorial Problem ◽  
Np Hard ◽  
Travelling Salesman ◽  
Shortest Route ◽  
Reproduction Process ◽  
Delivery Routes ◽  
Np Hard Problem ◽  
Selection Parameter

Abstract. Determining routes for ice tube delivery in Malang is a complex combinatorial problem classified as NP-hard problem. This study aims for optimizing the sales travel routes determination for the delivery to several customers by considering the efficiency of distance traveled. This problem is modeled in the form of Multi Salesman Traveling Problem. Genetic algorithm was used to optimize the determination of ice tube delivery routes that must be taken by each sales. Problems were coded by using permutation representation in which order crossover and swap mutation methods were used for the reproduction process. The process of finding solution was done by using elitism selection. The best genetic algorithm parameters obtained from the test results are the number of iterations of 40 and the population of 40, with the shortest route of 30.3 km. The final solution given by the genetic algorithm is in the form of a travel route that must be taken by each ice tube sales.Keywords: genetic algorithm, mutli travelling salesman problem, optimization, routeAbstrak. Penentuan rute pengiriman ice tube di kota Malang merupakan permasalahan kombinatorial kompleks yang diklasifikasikan sebagai permasalahan NP-hard. Penelitian ini bertujuan untuk melakukan optimasi dalam pembentukan rute perjalanan sales dalam melakukan pengiriman ke beberapa pelanggan dengan mempertimbangkan efisiensi jarak tempuh. Permasalahan ini dimodelkan dalam bentuk Multi Salesman Travelling Problem. Algoritme genetika digunakan untuk mengoptimalkan pembentukan rute pengiriman ice tube yang harus dilalui oleh setiap sales. Permasalahan dikodekan menggunakan representasi permutasi, dengan proses reproduksi menggunakan metode order crossover dan swap mutation. Proses pencarian solusi dilakukan menggunakan elitism selection. Parameter algoritme genetika terbaik yang didapatkan dari hasil pengujian adalah banyaknya iterasi sebesar 40 dan banyaknya populasi sebesar 40, dengan rute terpendek sebesar 30.3 km. Solusi akhir yang diberikan oleh algoritme genetika berupa rute perjalanan yang harus ditempuh oleh setiap sales ice tube.Kata Kunci: algoritme genetika, multi travelling salesman problem, optimasi, rute

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

scholarly journals Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Abid Hussain ◽  
Yousaf Shad Muhammad ◽  
M. Nauman Sajid ◽  
Ijaz Hussain ◽  
Alaa Mohamd Shoukry ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
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.

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

2017 ◽  
Vol 5 (2) ◽  
pp. 284-291
Author(s):  
Wafaa Mustafa Hameed ◽  
Asan Baker Kanbar
Keyword(s):  
Genetic Algorithms ◽  
Comparative Study ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Crossover Operator ◽  
Natural Evolution ◽  
Different Types ◽  
The Traveling Salesman Problem

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.

scholarly journals An improved cuckoo search algorithm with Stud crossover for Chinese TSP problem#

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Search Algorithm ◽  
Travelling Salesman Problem ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Cuckoo Search Algorithm ◽  
Theoretical Computer Science ◽  
Crossover Operator ◽  
Genetic Operators ◽  
Np Hard Problem ◽  
Improved Cuckoo Search Algorithm

The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.

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