A Genetic Algorithm with a Penalty Function in the Selective Travelling Salesman Problem on a Road Network

2011 ◽  
Author(s):  
Anna Piwonska ◽  
Franciszek Seredynski
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Road Network ◽  
Travelling Salesman Problem ◽  
Travelling Salesman

scholarly journals Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maha Ata Al-Furhud ◽  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Travelling Salesman ◽  
Complex Optimization ◽  
Problem Instances ◽  
Search Approach ◽  
Multiple Travelling Salesman Problem

The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

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 Fixed Task Scheduling of Industrial Robot using Genetic Algorithm Based Travelling Salesman Problem

2020 ◽  
Vol 9 (5) ◽  
pp. 1771-1775
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Shortest Path ◽  
Completion Time ◽  
Travelling Salesman Problem ◽  
Industrial Robots ◽  
Scheduling Problem ◽  
Search Technique ◽  
Travelling Salesman ◽  
Algorithm Search

The task scheduling of any industrial robots is a prior requirement to effectively use the capability by obtaining shortest path with optimum completion time. In this article, we have presented Travelling Salesman Problem (TSP) with Genetic Algorithm (GA) search technique based task scheduling technique for obtaining optimum shortest path of the task.TSP finds an optimal solution to search for the shortest route by considering every location for completing the required tasks by setting up GA. This article embrace the adaption and implementation of the Genetic Algorithm search strategy for the task scheduling problem in the cooperative control of multiple resources for getting shortest path with minimize the completion time for two zone specific task allocation problem. It can be inferred from the simulation results that the Genetic Algorithm search technique can be considered as a viable solution for the task scheduling problem.

scholarly journals Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

2019 ◽  
Vol 8 (2S4) ◽  
pp. 91-95
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Soft Computing Techniques

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.

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