• 한국경영과학회지
• /
• 제30권2호
• /
• pp.157-167
• /
• 2005

This paper presents a new perturbation technique for developing efficient iterated local search procedures for the asymmetric traveling salesman problem(ATSP). This perturbation technique uses global information on ATSP instances to speed-up computation and to improve the quality of the tours found by heuristic method. The main idea is to escape from a local optima by introducing perturbations on the out-of-kilter arcs in the problem instance. For a local search heuristic, we use the Kwon which finds optimum or near-optimum solutions by applying the out-of-kilter algorithm to the ATSP. The performance of our algorithm has been tested and compared with known method perturbing on randomly chosen arcs. A number of experiments has been executed both on the well-known TSPLIB instances for which the optimal tour length is known, and on randomly generated Instances. for 27 TSPLIB instances, the presented algorithm has found optimal tours on all instances. And it has effectively found tours near AP lower bound on randomly generated instances.

• 한국경영과학회지
• /
• 제31권3호
• /
• pp.41-51
• /
• 2006

Clustering is to group similar objects into clusters. Until now there are a lot of approaches using Self-Organizing feature Maps (SOFMS) But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of c output-layer nodes, if they want to make c clusters. This approach has problems to classify elaboratively. This Paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node's weight. We un find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. The proposed algorithm was tested on well-known IRIS data and TSPLIB. The results of this computational study demonstrate the superiority of the proposed algorithm.

• 한국콘텐츠학회논문지
• /
• 제20권11호
• /
• pp.636-643
• /
• 2020

차분 진화 알고리즘은 연속적인 문제 공간인 실수 최적화 문제를 해결하기 위해 개발된 메타휴리스틱 기법 중에 하나이다. 본 연구에서는 차분 진화 알고리즘을 불연속적인 문제 공간인 외판원 문제 해결에 사용하기 위하여 차분 진화 알고리즘에 난수 키 표현법을 적용하였다. 차분 진화 알고리즘은 실수 공간을 탐색하고 오름 차순으로 정렬된 해의 인덱스의 순서를 도시 방문 순서로 하여 적합도를 구한다. TSPLIB에서 제공하는 표준 외판원 문제에 적용하여 실험한 결과 제안한 난수 키 표현법 기반 차분 진화 알고리즘이 외판원 문제 해결에 가능성을 가지고 있음을 확인하였다.

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

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권4호
• /
• pp.217-222
• /
• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• 한국지능시스템학회논문지
• /
• 제15권6호
• /
• pp.661-667
• /
• 2005

본 논문에서는 서열순서화문제를 위한 새로운 혼합형 유전알고리즘을 제안한다. 제안된 유전알고리즘에서는 보로노이양자 화교차를 교차연산자로 사용하고 경로보전 3-최적화를 지역탐색 휴리스틱으로 사용한다. 보로노이양자화교차는 주어진 문제 인스턴스의 상위 정보를 이용하는 교차연산자이다. 이것은 원래 순회판매원문제를 위해서 제안된 교차연산자이기 때문에 서열순서화문제에 적용하기 위해서는 상당한 변형을 필요로 한다. 본 연구에서는 서열순서화문제에 맞도록 보로노이양자화교차를 적절히 변형하고, 변형된 보로노이양자화교차에서 필요로 하는 가능해생성알고리즘, 선행관계사이클분해알고리즘, 유전자거리지정방법 등을 개발하였다. TSPLIB와 ZIB-MP-Testdata로부터 얻어진 서열순서화문제 인스턴스들에 대한 실험결과, 제안된 유전알고리즘이 비교된 다른 유전알고리즘들에 비해서 더 안정적이고 성능이 우수한 것으로 나타났다.

• 한국컴퓨터정보학회논문지
• /
• 제27권2호
• /
• pp.1-8
• /
• 2022

본 논문에서는 개미 집단 시스템(ant colony system)을 통한 순회 외판원 문제(traveling salesman problem)를 효과적으로 해결하기 위해 GPU 기반 병렬 알고리즘을 설계 구현하였다. TSP에서 동시에 수백 또는 수천의 탐색 여정(tour)을 생성하는 반복 과정을 GPU의 작업 병렬성을 활용하여 처리성능을 개선하고, 페로몬 자취 데이터의 업데이트 과정은 32x32의 쓰레드 블럭을 사용하여 데이터 병렬성을 적극 활용하였다. 특히 다중 쓰레드의 메모리 동시 접근을 통해 연속 메모리공간의 병합 접근 효과와 공유 메모리의 동시 접근을 지원하였다. 본 실험은 TSPLIB에서 제공되는 127개부터 1002개에 이르는 도시 데이터를 사용하였고, Intel Core i9-9900K CPU와 Nvidia Titan RTX 시스템을 사용하여 순차 알고리즘과 병렬 알고리즘의 성능을 비교하였다. GPU 병렬화에 의한 성능 향상은 약 10.13~11.37배의 성능 개선 효과를 보였다.

• 한국경영과학회지
• /
• 제33권2호
• /
• pp.17-26
• /
• 2008

The traveling salesman problem is to find tours through all cities at minimum cost ; simply visiting the cities only once that a salesman wants to visit. As such, the traveling salesman problem is a NP-complete problem ; an heuristic algorithm is preferred to an exact algorithm. In this paper, we suggest an effective cost relaxation using a candidate arc set which is obtained from a regression function for the traveling salesman problem. The proposed method sufficiently consider the characteristics of cost of arcs compared to existing methods that randomly choose the arcs for relaxation. For test beds, we used 31 instances over 100 cities existing from TSPLIB and randomly generated 100 instances from well-known instance generators. For almost every instances, the proposed method has found efficiently better solutions than the existing method.

• 한국경영과학회지
• /
• 제32권3호
• /
• pp.47-61
• /
• 2007

This paper presents two elimination methods of subtours, which is obtained by applying the Out-of-Kilter algorithm to the sequential ordering problem (SOP) to produce a feasible solution for the SOP. Since the SOP is a kind of asymmetric traveling salesman problem (ATSP) with precedence constraints, we can apply the Out-of-Kilter algorithm to the SOP by relaxing the precedence constraints. Instead of patching subtours, both of two elimination methods construct a feasible solution of the SOP by using arcs constructing the subtours, and they improve solution by running 3-opt and 4-opt at each iteration. We also use a perturbation method. cost relaxation to explore a global solution. Six cases from two elimination methods are presented and their experimental results are compared to each other. The proposed algorithm found 32 best known solutions out of the 34 instances from the TSPLIB in a reasonable time.

• 대한안전경영과학회지
• /
• 제13권3호
• /
• pp.107-114
• /
• 2011

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.