A Survey on Hybridizing Genetic Algorithm with Dynamic Programming for Solving the Traveling Salesman Problem (original) (raw)
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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.
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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
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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...
Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm
Journal of Computer and Communications
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2020
The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff,...
Traveling Salesman Problem Solving by Hybridization of Genetic Algorithm and Ant System
International Journal of Computer and Electrical Engineering, 2012
Traveling salesman problem (TSP) is one of the most famous combinational optimization problems. Today, many solutions have been offered by using different methods to solve this problem. Each one of these solutions has its own advantages and disadvantages and a comprehensive solution which proves itself as the most optimum one is not presented yet. But we are still waiting for better solutions which solve the problem in more optimum ways. In this paper we have proposed a hybridization solution using the two GA and AS for TSP solving and we have called it GA-AS. The results of this new solution which is presented in experimental results, shows that TSP is solved by using our proposed combinational solution (GA-AS) has better results than TSP solved only by using standard GA. Another new idea considered in this paper is to change the current GA generation in order to reach a better generation and therefore a better answer which is explained schematically.
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.