• Journal of Korea Game Society
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• v.9 no.4
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• pp.57-66
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• 2009

A quest system is one of the important parts in the MMORPG (Massive Multiplayer Online Role Playing Game) contents. Because of its complexity in combining various content components, quest reward design belongs to a complicated work in estimating quest reward levels correctly in the initial development stage. In this paper, we suggest a new quest reward design technique by considering optimal quest play paths. We model a quest reward problem as the TSP (Traveling Salesman Problem) and solve that by adopting genetic algorithms. With our system, game designers easily estimate the optimal quest play path and it can be useful in reducing the trial-errors in the initial quest design process.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.1-8
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• 2022

In this paper, we design and implement a GPU-based parallel algorithm to effectively solve the traveling salesman problem through an ant color system. The repetition process of generating hundreds or thousands of tours simultaneously in TSP utilizes GPU's task-level parallelism, and the update process of pheromone trails data actively exploits data parallelism by 32x32 thread blocks. In particular, through simultaneous memory access of multiple threads, the coalesced accesses on continuous memory addresses and concurrent accesses on shared memory are supported. This experiment used 127 to 1002 city data provided by TSPLIB, and compared the performance of sequential and parallel algorithms by using Intel Core i9-9900K CPU and Nvidia Titan RTX system. Performance improvement by GPU parallelization shows speedup of about 10.13 to 11.37 times.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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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.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Journal of Ocean Engineering and Technology
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• v.18 no.2
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• pp.77-82
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• 2004

The purpose of this study is to develop a nesting algorithm, using a genetic algorithm to optimize nesting order, and modified No Fit Polygon(NFP) methodology to place parts with the order generated from the previous genetic algorithm. Various genetic algorithm techniques, which have thus far been applied to the Travelling Salesman Problem, were tested. The partially mapped crossover method, the inversion method for mutation, the elitist strategy, and the linear scaling method of fitness value were selected to optimize the nesting order. A modified NFP methodology, with improved searching capability for non-convex polygon, was applied repeatedly to the placement of parts according to the order generated from previous genetic algorithm. Modified NFP, combined with the genetic algorithms that have been proven in TSP, were applied to the nesting problem. For two example cases, the combined nesting algorithm, proposed in this study, shows better results than that from previous studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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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 the Korea Society of Computer and Information
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• v.24 no.11
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• pp.119-126
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• 2019

The partial inverse optimization problem is an interesting variant of the inverse optimization problem in which the given instance of an optimization problem need to be modified so that a prescribed partial solution can constitute a part of an optimal solution in the modified instance. In this paper, we consider the traveling salesman problem defined on the line (TSP on the line) which has many applications such as item delivery systems, the collection of objects from storage shelves, and so on. It is worth studying the partial inverse TSP on the line, defined as follows. We are given n requests on the line, and a sequence of k requests that need to be served consecutively. Each request has a specific position on the real line and should be served by the server traveling on the line. The task is to modify as little as possible the position vector associated with n requests so that the prescribed sequence can constitute a part of the optimal solution (minimum Hamiltonian cycle) of TSP on the line. In this paper, we show that the partial inverse TSP on the line and its variant can be solved in polynomial time when the sever is equiped with a specific internal algorithm Forward Trip or with a general optimal algorithm.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.55-61
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• 2020

Genetic Algorithm(GA) is applied to a problem that could not figure out its solution in a straightway. It is called as NP-hard problem. GA requires a high-performance system to be run on since the high-cost operations are needed such as crossover, selection, and mutation. Moreover, the scale of the problem domain is normally huge. That is why the straightway cannot be applied. To reduce the drawback of high-cost requirements, we try to answer if all the operations including mutation are necessary for all cases. In the experiment, we set up two cases of with/without mutation operations and gather the number of generations and the fitness of a solution. The subject in the experiment is Travelling Salesman Problem(TSP), which is one of the popular problems solved by GA. As a result, the cases with mutation operation are not faster and the solution is fitter than the case with mutation operation. From the result, the conclusion is that mutation operation does not always need for a better solution in a faster way.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1070-1073
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• 2002

This study presents a parallel branch-and-bound (PBAB) method for traveling salesman problem (TSP). The PBAB method adopts intermediate form of central control and distributed control in terms of the lightness of the master process's role. Compared with fully distributed control, the control scheme involves less concentration of communication on the master. Moreover, in order to reduce the influence of communication, the worker is composed of a computation thread and a communication thread. The multithreadness realizes the almost blocking free communications on the master. We implement the proposed PBAB method on a network of PCs, which consists of one master and up to 16 workers. We experiment five TSP instances. The results shows that the efficiency increases with the problem size.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.1-10
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• 2000

The traveling salesman problem is a representative NP-Complete problem. It needs lots of time to get a solution as the number of city increase. So, we need an efficient heuristic algorithm that gets good solution in a short time. Almost edges that participate in optimal path have somewhat low value cost. This paper discusses the property of nearest neighbor and 3-opt. This paper uses nearest neighbor's property to select candidate edge. Candidate edge is a set of edge that has high probability to improve cycle path. We insert edge that is one of candidate edge into intial cycle path. As two cities are connected. It does not satisfy hamiltonian cycle's rule that every city must be visited and departed only one time. This paper uses 3-opt's method to sustain hamiltonian cycle while inserting edge into cycle path. This paper presents a highly efficient heuristic algorithm verified by numerous experiments.