• International Journal of Computer Science & Network Security
• /
• v.24 no.3
• /
• pp.12-22
• /
• 2024

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1993.04a
• /
• pp.354-366
• /
• 1993

In this paper, new algorithms for solving the multiple traveling salesman problem with time windows are presented. These algorithms are based on the flow based algorithms for solving the vehicle scheduling problem. Computational results on problems up to 750 customers indicate that these algorithms produce superior results to existing heuristic algorithms for solving the vehicle routing problems when the time windows are 'tight enough' where 'tight enough' is based on a metric proposed by desrosiers et al.(1987).

• Journal of the Korean Operations Research and Management Science Society
• /
• v.30 no.2
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.61
• /
• pp.127-135
• /
• 2000

This paper provides a new method of initializing neurons used in self-organizing neural networks and sequencing input nodes for applying to Euclidean traveling salesman problem. We use a general property that in any optimal solution for Euclidean traveling salesman problem, vertices located on the convex hull are visited in the order in which they appear on the convex hull boundary. We composite input nodes as number of convex hulls and initialize neurons as shape of the external convex hull. And then adapt input nodes as the convex hull unit and all convex hulls are adapted as same pattern, clockwise or counterclockwise. As a result of our experiments, we obtain l∼3 % improved solutions and these solutions can be used for initial solutions of any global search algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.10a
• /
• pp.783-786
• /
• 2008

외판원문제(Traveling Salesman problem: TSP)는 전형적인 조합최적화 문제로 위치하는 n개의 모든 지점을 오직 한번씩만 방문하는 순회경로를 결정하는 과정에서 순회비용 또는 순회거리를 최소화한다. 따라서 본 논문에서는 종래의 NP-hard문제로 널리 알려진 TSP를 해결하기 위해서 메타 휴리스틱기법 중에서 가장 널리 이용되고 있는 유전 알고리즘(Genetic Algorithm: GA)을 이용한다. 마지막으로, 유전 알고리즘을 이용해 외판원문제에 적합한 성능을 보이는 유전 연산자를 찾아내기 위해 수치 실험을 통해 그 성능에 대한 평가를 한다.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.983-985
• /
• 1995

In this paper, we propose a Self Organizing Feature Map (SOFM) Type Neural Computation Algorithm for the Travelling Salesman Problem(TSP). The actual best solution to the TSP problem is computatinally very hard. The reason is that it has many local minim points. Until now, in neural computation field, Hopield-Tank type algorithm is widely used for the TSP. SOFM and Elastic Net algorithm are other attempts for the TSP. In order to apply SOFM type neural computation algorithms to the TSP, the object function forms a euclidean norm between two vectors. We propose a Largrangian for the above request, and induce a learning equation. Experimental results represent that feasible solutions would be taken with the proposed algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11a
• /
• pp.323-326
• /
• 2002

DNA 컴퓨팅은 생체 분자들이 갖는 막대한 병렬성을 이용하여 조합 최적화 문제에 적용하는 연구가 많이 시도되고 있다. 특히 TSP(Travelling Salesman Problem)는 간선에 대한 가중치 정보가 추가되어 있기 때문에 가중치를 DNA 염기 배열로 표현하기 위한 효율저인 방법들이 제시되지 않았다. 따라서 본 논문에서는 DNA 컴퓨팅에 DNA 코딩 방법을 적용하여 정점과 간선을 효율적으로 생성하고 표현된 DNA 염기 배열의 간선에 실제간을 적용하여 가중치 정보를 계산하는 ACO(Algorithm for Code Optimization)를 제안한다. DNA 코딩 방법은 변형된 유전자 알고리즘으로 DNA 기능을 유지하며, 서열의 길이를 줄일 수 있으므로 최적의 서열을 생성할 수 있는 특징을 갖는다. 실험에서 ACO를 TSP에 적용하여 Adleman의 DNA 컴퓨팅 알고리즘과 비교하였다. 그 결과 초기 문제 표현에서 우수한 적합도 값을 생성했으며, 경로의 변화에도 능동적으로 대처하여 최적의 결과를 빠르게 탐색할 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.6
• /
• pp.2269-2275
• /
• 2010

The performance of Genetic Algorithms (GA) is affected by various factors such as parameters, genetic operators and strategies. The traditional approach with random initial population is efficient however the whole initial population may contain many infeasible solutions. Thus it would take a long time for GA to produce a good solution. The GA have been modified in various ways to achieve faster convergence and it was particularly recognized by researchers that initial population greatly affects the performance of GA. This study proposes modified GA with sorted initial population and applies it to solving Travelling Salesman Problem (TSP). Normally, the bigger the initial the population is the more computationally expensive the calculation becomes with each generation. New approach allows reducing the size of the initial problem and thus achieve faster convergence. The proposed approach is tested on a simulator built using object-oriented approach and the test results prove the validity of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.3
• /
• pp.622-627
• /
• 2006

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.3
• /
• pp.1-7
• /
• 2019

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.