scholarly journals Aspects of the Study of Genetic Algorithms and Mechanisms for their Optimization for the Travelling Salesman Problem

2021 ◽  
pp. 543-550
Author(s):  
Nataliya Boyko ◽  
Andriy Pytel
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Biological Evolution ◽  
Travelling Salesman ◽  
Unconstrained Optimization Problems ◽  
The Common ◽  
Common Problems ◽  
Constrained And Unconstrained Optimization

Lately, artificial intelligence has become increasingly popular. Still, at the same time, a stereotype has been formed that AI is based solely on neural networks, even though a neural network is only one of the numerous directions of artificial intelligence. This paper aims to bring attention to other directions of AI, such as genetic algorithms. In this paper, we study the process of solving the travelling salesman problem (TSP) via genetic algorithms (GA) and consider the issues of this method. The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that are based on natural selection, the process that drives biological evolution. One of the common problems in programming is the travelling salesman problem. Many methods can be used to solve it, but we are going consider genetic algorithms. This study aims at developing the most efficient application of genetic algorithms in the travelling salesman problem.

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

Recombination Operators in Permutation-Based Evolutionary Algorithms for the Travelling Salesman Problem

2012 ◽  
pp. 268-285
Author(s):  
Camelia Chira ◽  
Anca Gog
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Genetic Material ◽  
Hard Problem ◽  
Travelling Salesman ◽  
Routing And Scheduling ◽  
Np Hard Problem ◽  
Account Information ◽  
Ant Colony Systems

The Travelling Salesman Problem (TSP) is one of the most widely studied optimization problems due to its many applications in domains such as logistics, planning, routing, and scheduling. Approximation algorithms to address this NP-hard problem include genetic algorithms, ant colony systems, and simulated annealing. This chapter concentrates on the evolutionary approaches to TSP based on permutation encoded individuals. A comparative analysis of several recombination operators is presented based on computational experiments for TSP instances and a generalized version of TSP. Numerical results emphasize a good performance of two proposed crossover schemes: best-worst recombination and best order recombination which take into account information from the global best and/or worst individuals besides the genetic material from parents.

Optimized Solution of TSP (Travelling Salesman Problem) Based on Mendelian Inheritance

2020 ◽  
Vol 13 (5) ◽  
pp. 909-916
Author(s):  
Vivek Sharma ◽  
Rakesh Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Genetic Algorithms ◽  
Operation Research ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Mendelian Inheritance ◽  
Travelling Salesman ◽  
Ultimate Objective

Background: TSP problem has been the part of literature from many decades; it’s an important optimization issue in operation research. TSP problem always remain greedy for the better results especially if chosen working field are Genetic Algorithms (GA). Objective: This paper presents a TSP solution, which performed the modified selection and crossover operations as well as takes advantage of Mendelian inheritance while producing the generations. Methods: GA has very broad resolution scope for optimization problems and it is capable enough for generating well-optimized results if right GA technique has been applied on right point of issue in controlled manner. here the proposed agenda is to utilize the GA concept for TSP by applying mendels rules which is never applied before for the same issue. Here the proposed scheme applies some modification in traditional Mendel process. In general, full chromosome window has been utilized in mendel inheritance process but in presented scheme we have utilizes Most Significant Bits (MSB) only which helps in to control the convergence aptitude of the process. Results: The scheme uses advanced modified Mendel operation which helps in to control convergence aptitude of the operation. It efficiently minimizes the total travelled distance of the graph which was the ultimate objective of the problem and that has been successfully achieved. Conclusion: The validation of the scheme has been confirmed from the obtained results, which are better enough as comparison to traditional TSP-GA.

scholarly journals Optimal recombination in genetic algorithms for combinatorial optimization problems: Part II

2014 ◽  
Vol 24 (2) ◽  
pp. 165-186 ◽  
Author(s):  
Anton Eremeev ◽  
Julia Kovalenko
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Makespan Minimization ◽  
Travelling Salesman ◽  
Hamilton Path ◽  
Combinatorial Optimization Problems ◽  
Optimal Recombination

This paper surveys results on complexity of the optimal recombination problem (ORP), which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. In Part II, we consider the computational complexity of ORPs arising in genetic algorithms for problems on permutations: the Travelling Salesman Problem, the Shortest Hamilton Path Problem and the Makespan Minimization on Single Machine and some other related problems. The analysis indicates that the corresponding ORPs are NP-hard, but solvable by faster algorithms, compared to the problems they are derived from.

scholarly journals Perancangan Aplikasi Penjadwalan Traveling Salesman Problem dengan Algoritma Genetika

2012 ◽  
Vol 3 (2) ◽  
pp. 977
Author(s):  
Hendy Tannady ◽  
Andrew Verrayo Limas
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Optimal Sequence ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
A Company

Supply chain management plays an important role in enhancing the efficiency and effectiveness of manufacturing industry business process. In this research, the problem is taken from a sales division in a company in determining the optimal sequence when delivering goods into nine cities. This problem is oftenreferred as travelling salesman problem. This problem is considered important since the optimal sequence can cut off operational cost. Creating an artificial intelligence for the company in determining the location and the optimal sequence of delivering goods is the main objective of this research. A genetic algorithm is utilized to determine the location and the optimal sequence. While for processing the data and concluding the result, researcher designed a Java-based application that provides the capability of automatic computing. The result of this computation is a sequence of locations with a fitness number for each. The best fitness number for the sequence location will be used for the final result and the conclusion to answer the company’s problem.

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.

DIMMA

2010 ◽  
Vol 1 (4) ◽  
pp. 57-74 ◽  
Author(s):  
Masoud Yaghini ◽  
Rahim Akhavan
Keyword(s):  
Tabu Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Metaheuristic Algorithms ◽  
Standard Process ◽  
Travelling Salesman ◽  
Design And Implementation ◽  
The Future ◽  
Implementation Methodology

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.

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.

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