Optimization Approach Based on Immigration Strategies for Symmetric Traveling Salesman Problem (original) (raw)

Genetic Algorithm Adopting Immigration Operator to Solve the Asymmetric Traveling Salesman Problem

International Journal of Pure and Apllied Mathematics, 2017

In this work, we are interested in improving the performance of genetic algorithm (GA) to solve the Asymmetric Traveling Salesman Problem (ATSP). Several approaches have been developed with genetic algorithms based on the adaptation and improvement of different standard genetic operators. We proposes a new GA adopting immigration strategies to maitain diversity and to perform more the genetic algorithm. Experimental results on series of standard instances of ATSP show that the proposed structured memory immigration scheme in GA effectively improves the performance of GAs.

Application Of Genetic Algorithm to Solve Traveling salesman problem

2015

This research investigated the application of Genetic Algorithm capable of solving the traveling salesman problem (TSP). Genetic Algorithm are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer Simulations demonstrate that the Genetic Algorithm is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks, and evolutionary computation of the successful use of a natural metaphor to design an optimization algorithm. A study of the genetic algorithm explains its performance and shows that it may be seen as a parallel variation of tabu search, with an implicit memory. Genetic algorithm is the most efficient in computational time but least efficient in memory consumption. The Genetic algorithm differs from the nearest neighbourhood heuristic in that it considers the neares...

Traveling Salesman Problem of Optimization based on Genetic Algorithms

Traveling Salesman Problem consists in finding the shortest distance commercial representatives should undertake on visiting the 24 cities in Tunisia prior to resuming the initial departure point. Such a deceptive issue appears to stand as a remarkable challenge in computational mathematics. The purpose of this paper lies in implementing genetic Mat lab's algorithms toolbox gads in a bid to cope with such a problem.

An Improved Genetic Algorithm to Solve the Traveling Salesman Problem

An improved immune-genetic algorithm is applied to solve the traveling salesman problem (TSP) in this paper. A new selection strategy is incorporated into the conventional genetic algorithm to improve the performance of genetic algorithm. The selection strategy includes three computational procedures: evaluating the diversity of genes, calculating the percentage of genes, and computing the selection probability of genes. Computer numerical experiments on two case studies (21-city and 56-city TSPs) are performed to validate the effectiveness of the improved immune-genetic algorithm. The results show that by incorporating inoculating genes into conventional procedures of genetic algorithm, the number of evolutional iterations to reach an optimal solution can be significantly reduced.

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.

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

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.

An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

Engineering, Technology & Applied Science Research, 2017

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the publish...

Solving Traveling Salesman Problem Using Genetic Algorithm Based on Efficient Mutation Operator

2021

The Traveling Salesman Problem (TSP) is a Combinatorial Optimization Problem (COP), which belongs to NP-hard problems and is considered a typical problem for many real-world applications. Many researchers used the Genetic Algorithm (GA) for solving the TSP. However, using a suitable mutation was one of the main obstacles for GA. This paper proposes for GA an Efficient Mutation (GA-EM) for solving TSP. The efficient mutation can balance between deeply searching and preventing stuck on local optima to ensure a better convergence rate and diversity. Therefore, in this paper, a local search method based on three neighborhood structure operators; namely, transpose, shift-and-insert, and swap, is proposed to produce the efficient mutation for GA. The performance of the proposed algorithm is validated by three TSP datasets; including, TSPLIB, National TSPs, and VLSI Data Set. These datasets have different graphs’ structures and sizes. The sizes of the datasets range from 150 to 18512 citie...

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.