• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.193-201
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• 2023

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제8권5호
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• pp.1279-1288
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• 2007

A vehicle routing problem with time constraint is one of the must important problem in distribution and logistics. In practice, the service for a customer must start and finish within a given delivery time. This study is concerned about the development of a model to optimize vehicle routing problem under the multi-logistics center problem. And we used a two-step approach with an improved genetic algorithm. In step one, a sector clustering model is developed by transfer the multi-logistics center problem to a single logistics center problem which is more easy to be solved. In step two, we developed a GA-TSP model with an improved genetic algorithm which can search a optimize vehicle routing with given time constraints. As a result, we developed a Network VRP computer programs according to the proposed solution VRP used ActiveX and distributed object technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.249-257
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• 2015

This paper introduces a heuristic algorithm to NP-hard travelling salesman problem. The proposed algorithm, in its bid to determine initial path, applies SW-DNN, DW-DNN, and DC-DNN, which are modified forms of the prevalent Double-sided Nearest Neighbor Search and searches the minimum value. As a part of its optimization process on the initial solution, it employs 2, 2.5, 3-opt of a local search k-opt on candidate delete edges and 4-opt on undeleted ones among them. When tested on TSP-1 of 26 European cities and TSP-2 of 49 U.S. cities, the proposed algorithm has successfully obtained optimal results in both, disproving the prevalent disbelief in the attainability of the optimal solution and making itself available as a general algorithm for the travelling salesman problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제20권41호
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• pp.177-188
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• 1997

This study describes the important information for establishing Human Computer Interface System for solving the large scale Traveling Saleman Problem in Printed Circuit Board production. Appropriate types and sizes of partitioning of large scale problems are discussed. Optimal tours for the special patterns appeared in PCB's are given. The comparision of optimal solutions of non-Euclidean problems and Euclidean problems shows the possibilities of using human interface in solving the Chebyshev TSP. Algorithm for the large scale problem using described information and coputational result of the practical problem are given.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제33권4호
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (1)
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• pp.61-63
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• 2003

지난 10년 동안 유전 알고리즘은 어렵고 복잡한 다양한 문제들을 해결하기 위한 새로운 방법으로 인식되어왔다. 이러한 유전 알고리즘의 성능은 알고리즘 내에 구현되는 여러 연산자들에 좌우된다. 따라서 많은 연구자들이 새로운 연산자 개발에 관심을 가져 왔었다. 특히, 가장 널리 알려진 조합최적화 문제 중에 하나인 알려진 traveling salesman problem (TSP)의 경우 NP-hard문제로 분류되어 현재까지 이를 해결하기 위한 다양한 유전 연산자들이 개발되어 왔었다. 따라서 본 논문에서는 TSP 문제를 test problem로 이용하여 이를 해결하기 위한 새로운 유전 연산자 특히 교차 (Crossover Operator) 연산자들을 제안하고 기존의 다양한 연산자들과 비교를 통해서 성능을 입증한다.

• Journal of Navigation and Port Research
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• 제29권2호
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• pp.141-146
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• 2005

For a long time, genetic algorithms have been recognized as a new method to solve difficult and complex problems and the performance of genetic algorithms depends on genetic operators, especially crossover operator. Various problems like the traveling salesman problem, the transportation problem or the job shop problem, in logistics engineering can be modeled as a sequencing problem This paper proposes modified genetic crossover operators to be used at various sequencing problems and uses the traveling salesman problem to be applied to a real world problem like the delivery problem and the vehicle routing problem as a benchmark problem Because the proposed operators use parental information as well as network information, they could show better efficiency in performance and computation time than conventional operators.

• The KIPS Transactions:PartB
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• 제13B권3호
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• pp.315-322
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• 2006

Ant Colony System(ACS) is easily applicable to the traveling salesman problem(TSP) and it has demonstrated good performance on TSP. Recently, ACS has been emerged as the useful tool for the pattern recognition, feature extraction, and edge detection. The edge detection is wifely utilized in the area of document analysis, character recognition, and face recognition. However, the conventional operator-based edge detection approaches require additional postprocessing steps for the application. In the present study, in order to overcome this shortcoming, we have proposed the new ACS-based edge detection algorithm. The experimental results indicate that this proposed algorithm has the excellent performance in terms of robustness and flexibility.

• The KIPS Transactions:PartA
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• 제10A권4호
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• pp.369-374
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• 2003

Ant Algorithm is used to find the solution of Combinatorial Optimization Problems. Real ants are capable of finding the shortest path from a food source to their nest without using visual informations. This behavior of real ants has inspired ant algorithm. There are various versions of Ant Algorithm. Ant Colony System (ACS) is introduced lately. ACS is applied to the Traveling Salesman Problem (TSP) for verifying the availability of ACS and evaluating the performance of ACS. ACS find a good solution for TSP When ACS is applied to different Combinatorial Optimization Problems, ACS uses the same parameters and strategies that were used for TSP. In this paper, ACS is applied to the Multicast Routing Problem. This Problem is to find the paths from a source to all destination nodes. This definition differs from that of TSP and differs from finding paths which are the shortest paths from source node to each destination nodes. We introduce parameters and strategies of ACS for Multicasting Routing Problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 대한산업공학회/한국경영과학회 1994년도 춘계공동학술대회논문집; 창원대학교; 08월 09일 Apr. 1994
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• pp.328-338
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• 1994

TSP(Traveling Salesman Problem) is a famous problem in Operations Research fields due to its applicability to various problems. It is also well-known that the problem is hard to solve in reasonable time, since it is in the NP-Complete class. Hence it is desired to develop heuristics which have polynominal complexity and also solve the problem to near-optimality. This paper presents a heuristic algorithm for TSP using the concept of dynamic programming. The proposed method has the complexity of O(N$\^$3/), and gives improved solutions than other well-known algorithms in our extensive computational experiments.