A HYBRID GENETIC ALGORITHM — A NEW APPROACH TO SOLVE TRAVELING SALESMAN PROBLEM (original) (raw)
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An efficient hybrid genetic algorithm for the traveling salesman problem
Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 2000
This paper describes an efficient hybrid genetic algorithm (HGA) for the traveling salesman problem. In general, a genetic algorithm (GA) combined with other algorithms (e.g., a local search) is well known to be a powerful approach. The other algorithms are divided into local search heuristics and metaheuristics. In incorporating the metaheuristics, it is reported that a difficult problem of changing processes between the GA process and the metaheuristic search process appears. To avoid the difficult problem using simulated annealing as one of the metaheuristics, we investigate an efficient HGA that does not involve the problem. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(2): 7683, 2001
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.
Traveling Salesman Problem (TSP) is a well-known NP-hard problem. Many algorithms were developed to solve this prob-lem and gave the nearly optimal solutions within reasonable time. This paper presents a survey about the combination Genetic Algorithm (GA) with Dynamic Programming (DP) for solving TSP. We also setup a combination between GA and DP for this problem and experimented on 7 Euclidean instances derived from TSP-lib. Experimental results are reported to show the efficiency of the experimented algorithm comparing to the genetic algorithm.
Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm
Journal of Computer and Communications
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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.
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...
Genetic Algorithm to Traveling Salesman Problem
Network and Complex Systems, 2016
In this paper, we apply a genetic algorithm to TSP. Since in TSP, a tour must pass through edges in E' ( E) at least once, it is necessary to involve E' and the information of direction in the chromosome. However, if we use the existing chromosome structure, the length of the chromosome becomes 2 jE0j and the size of the solution space becomes 2jE0j jE0j!. In the previous study, since the chromosome uses two kinds of information (E' and the direction), the results and the time to find a near-optimal solution vary according to the method of applying genetic operators. To resolve these defects, this paper proposes a new structure of chromosome for TSP.
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 Study of Genetic and Heuristic Algorithm for Traveling Salesman Problem
2014
Genetic algorithm is an algorithm that behaves in a way similar to the evolution process of the mankind, i.e. the crossing of the chromosomes. Heuristic algorithm is another algorithm of Artificial Intelligence that improves the speed of solving a problem. They are very common Algorithms in Artificial Intelligence that are used for various complex problems. Traveling Salesman Problem is NP- Hard problem in combinatorial optimization that finds its applications in manufacture of microchips, planning, logistics etc. In this paper we study the Genetic Algorithm and Heuristic algorithm for Traveling Salesman Problem individually, with their respective drawbacks. In the next part of the paper we compare both the algorithms with respect to the traveling salesman Problem. Finally we study the combination algorithm of Genetic algorithm and Heuristic algorithm, again for the Traveling Salesman Problem and give its advantages over both the algorithms.