Genetic Algorithm to Traveling Salesman Problem (original) (raw)

Genetic algorithm and a double-chromosome implementation to the traveling salesman problem

SN Applied Sciences, 2019

The variety of methods used to solve the traveling salesman problem attests to the fact that the problem is still vibrant and of concern to researchers in this area. For problems with a large search space, similar to the traveling salesman problem, evolutionary algorithms such as genetic algorithm are very powerful and can be used to obtain optimized solutions. However, the challenge in applying a genetic algorithm to the traveling salesman problem is the choice of appropriate operators that could produce legal tours. In the literature, additional repair algorithms have been introduced and employed and the offspring produced by these genetic algorithm operators are modified to ensure that the generated chromosomes represent legal tours. Rather than sticking to repair algorithms, a double-chromosome approach is proposed in this article. The proposed method can be employed to optimize problems similar to the traveling salesman problem. The double-chromosome approach has been tested with a variety of traveling salesman problems, and the results indicated that the proposed method has a high rate of convergence toward the shortest tour.

Solving the Traveling Salesman Problem using Genetic Algorithms

The Traveling Salesman Problem (TSP) is solved using genetic algorithms. Two alternative ways to represent the solution (genes in series and genes in two columns) are used and different mutation rates as well as different population sizes are considered. Two computer programs are created to solve the problem and yield experimental data. The data is analyzed and discussed and conclusions are derived. An equation relating the percentage of the population with the same solution (w) and the mutation rate (g) is discussed. This equation and the tradeoff between population size, mutation rate and chromosome portraying method are the theoretical contributions of this work, as well as an innovative way to solve the TSP, which is a hard problem to solve using traditional optimization, such as Linear Programming.

The Application of Genetic Algorithm in Solving Traveling Salesman Problem

2020

Traveling Salesman Problem (TSP) is one form of optimization problem with easy concept, but complicated if solved conventionally. The purpose of TSP is to build an optimal routes, with the rules of each city to be visited by salesmen and the cities are visited only exactly once, the trip begins and ends in the city early. To build the optimal routes, in this study using genetic algorithm. In the example case there are 4 cities that must be traversed by the salesman, that city A, B, C, and D with the trip starts from the city A and ends in city A as well. So obtained the optimal route that is [A D B C] with minimum distance that is 19 km.

Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms

American Journal of Applied Sciences, 2009

Problem statement: Genetic Algorithms (GAs) have been used as search algorithms to find near-optimal solutions for many NP problems. GAs require effective chromosome representations as well as carefully designed crossover and mutation operators to achieve an efficient search. The Traveling Salesman Problem (TSP), as an NP search problem, involves finding the shortest Hamiltonian Path or Cycle in a graph of N cities. The main objective of this study was to propose a new representation method of chromosomes using upper triangle binary matrices and a new crossover operator to be used as a heuristic method to find near-optimum solutions for the TSP. Approach: A proposed genetic algorithm, that employed these new methods of representation and crossover operator, had been implemented using DELPHI programming language on a personal computer. Also, for the purpose of comparisons, the genetic algorithm of Sneiw had been implemented using the same programming language on the same computer. Results: The outcomes obtained from running the proposed genetic algorithm on several TSP instances taken from the TSPLIB had showed that proposed methods found optimum solution of many TSP benchmark problems and near optimum of the others. Conclusion: Proposed chromosome representation minimized the memory space requirements and proposed genetic crossover operator improved the quality of the solutions in significantly less time in comparison with Sneiw's genetic algorithm.

A new genetic approach for the traveling salesman problem

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

A new genetic algorithm (GA) for the Traveling Salesman Problem (TSP) is given. Two novel features of this algorithm are: (i) a new locus-based encoding/crossover pair, and (ii) a static preprocessing step which changes the e " g order of the vertices. It is believed that this algorithm is also applicable to other ordering problems, not just TSP. Experimental results on the standard benchmarks for TSP are favorable.

A HYBRID GENETIC ALGORITHM — A NEW APPROACH TO SOLVE TRAVELING SALESMAN PROBLEM

International Journal of Computational Engineering Science, 2001

This paper introduces three new heuristics for the Euclidean Traveling Salesman Problem (TSP). One of the heuristics called Initialization Heuristics (IH) is applicable only to the Euclidean TSP, while other two heuristics RemoveSharp and LocalOpt can be applied to all forms of symmetric and asymmetric TSPs. A Hybrid Genetic Algorithm (HGA) has been designed by combining a variant of an

An Approach to the Travelling Salesman Problem using Genetic Algorithm

Journal of emerging technologies and innovative research, 2019

In this paper we have presented a solution for the travelling salesman problem using genetic algorithm. The solution provides a maximal approximation of the problem along with cost reduction. The solution also presents a method to find the nearly optimized solution for these types of optimization problems using a new crossover technique that produces a high quality solution to the TSP. Later, the paper presents a comparison of the effectiveness of the crossover operator with some traditional crossover operators.

Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm

Journal of Computer and Communications

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Mechanism Design and Analysis of Genetic Operations in Solving Traveling Salesman Problems

Lecture Notes in Computer Science, 2006

In this paper, some novel improved genetic operations are presented, several combinations of genetic operations are examined and the functions of these operations at different evolutionary stages are analyzed by numerical experiments. The essentiality of the ordering of the gene section, the significance of the evolutionary inversion operation and the importance of the selection model are discussed. Some results provide useful information for the implementation of the genetic operations for solving the traveling salesman problem.