• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.9-11
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• 1999

조합 최적화 문제인 Traveling Salesman problems(TSP)을 Genetic Algorithm(GA)과 Local Search Heuristic인 Lin-Kernighan(LK) Heuristic[2]을 이용하여 접근하는 것은 최적해를 구하기 위해 널리 알려진 방법이다. 이 논문에서는 LK를 이용하여 주어진 TSP 문제에서 Local Optima를 찾고, GA를 이용하여 Local Optimal를 바탕으로 Global Optima를 찾는데 이용하게 된다. 여기서 이런 GA와 LK를 이용하여 TSP 문제를 풀 경우 해가 점점 수렴해가면서 중복된 유전자가 많이 생성된다. 이런 중복된 유전자를 제거함으로써 탐색의 범위를 보다 넓고 다양하게 검색하고, 더욱 효율적으로 최적화를 찾아내는 방법에 대해서 논하겠다. 이런 방법을 이용하여 rat195, gil262, lin318의 TSP문제에서 효율적으로 수행된다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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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 Korea Information Processing Society Conference
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• 2000.10a
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• pp.321-324
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• 2000

조합 최적화 문제인 Traveling Salesman problems(TSP)을 Genetic Algorithm(GA)[3]과 Local Search Heuristic Algorithm[8]을 이용하여 접근하는 것은 최적해를 구하기 위해 널리 알려진 방법이다. 본 논문에서는 TSP문제를 해결하기 위한 또 다른 접근법으로, 다수의 Ant들이 Tour들을 찾는 ACS(Ant Colony System) Algorithms[4][6][7]을 소개하고, ACS에서 Global Optima를 찾는 과정에서, 이미 이루어져 있는 Ant들의 Tour결과들을 서로 비교한다. Global Updating Rule에 의해 Global Best Tour 에 속해 있는 각 Ant Tour의 edge들을 update하는 ACS Algorithm에, 각 루프마다 Ant Tour들을 우성과 열성 인자들로 구분하고, 각각의 우성과 열성 인자들에 대해서 Global Updating Rule에 기반한 가중치를 적용(Weight Updating Rule)하므로서 기존의 ACS Algorithm보다 효율적으로 최적 해를 찾아내는 방법에 대해서 논하고자 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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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.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.278-278
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• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve optimization problems such as the Traveling Salesman Problem(TSP) and maximizing or minimizing functions. In particular, because TSP is a well-known combinational optimization problem andbelongs to a NP-complete problem, there is huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. Additionally, the non-schema part is applied to inversion for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.

• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.132-139
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• 2016

In this paper, a minimum time path planning strategy is proposed for multi points manufacturing problems in drilling/spot welding tasks. By optimizing the travelling schedule of the set points and the detailed transfer path between points, the minimum time manufacturing task is realized under fully utilizing the dynamic performance of robotic manipulator. According to the start-stop movement in drilling/spot welding task, the path planning problem can be converted into a traveling salesman problem (TSP) and a series of point to point minimum time transfer path planning problems. Cubic Hermite interpolation polynomial is used to parameterize the transfer path and then the path parameters are optimized to obtain minimum point to point transfer time. A new TSP with minimum time index is constructed by using point-point transfer time as the TSP parameter. The classical genetic algorithm (GA) is applied to obtain the optimal travelling schedule. Several minimum time drilling tasks of a 3-DOF robotic manipulator are used as examples to demonstrate the effectiveness of the proposed approach.