• 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 KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.2
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• pp.180-187
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• 2010

In this paper, a global path generation method is suggested using multiple grid maps connected with the mission type of unmanned combat vehicle(UCV). In order to carry out a mission for UCV, it is essential to find a global path which is coincident with the characteristics of the mission. This can be done by considering various combat circumstances represented as grid maps such as velocity map, threat map and communication map. Cost functions of multiple grid maps are linearly combined and normalized to them simultaneously for the path generation. The proposed method is realized using $A^*$, a well known search algorithm, and cost functions are normalized in the ratio of the traverse time which is one of critical information should be provided with the operators using the velocity map. By the experiments, it is checked found global paths match with the mission type by reflecting input data of grid maps properly and the computation time is short enough to regenerate paths in real time as combat circumstances change.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.105-111
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• 2004

DNA computing has been using to solve TSP (Traveling Salesman Problems). However, when the typical DNA computing is applied to TSP, it can`t efficiently express vertices and weights of between vertices. In this paper, we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to efficiently express vertices and weights of between vertices for TSP. We applied ACO to TSP and as a result ACO could express DNA codes which have variable lengths and weights of between vertices more efficiently than Adleman`s DNA computing algorithm could. In addition, compared to Adleman`s DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for a shortest path in a short time.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.81-82
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• 2006

Most work on cooperative diversity has assumed that the cooperating group (source and partners) and the associated average channel conditions between terminals (source, partners, and destination) are predetermined. In practical situations, however, it is important to develop the efficient algorithms for assigning the terminals with good inter-user channels for cooperating groups. In this paper, we propose the partner assignment algorithm for cooperative diversity in mobile communication systems. The proposed partner assignment algorithm is investigated by using the path loss model for mobile communication systems. Numerical results show that the proposed partner assignment algorithm provides the comparable probability of cooperative transmission to the partner assignment algorithm using exhaustive search. The probability of cooperative transmission increases with the number of users, which gives potential benefits of practical implementation to user cooperation in mobile communication systems.

• Annual Conference of KIPS
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• 2001.04b
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• pp.1165-1168
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• 2001

최단경로문제(Shortest Path Problem)는 네트???p에서 하나 혹은 그 이상의 노드들의 쌍 사이에서 가장 짧은 경로, 가장 저렴한 경로 또는 가장 신뢰할 만한 경로를 찾을 때 고려된다. 컴퓨터나 통신망들은 edge-weighted 그래프로 대치될 수 있으며 그렇게 함으로써 최단 경로를 찾아줄 수 있다. 통신 링크는 실제 실패할 수도 있고, 또한 전송될 데이터의 양에 따라 전달되는 시간이 달라지기도 하므로, 가장 신뢰할만한 경로 중에서 가장 빠른 경로(The Quickest Most Reliable Path) 문제와 가장 빠른 경로 중에서 가장 신뢰할만한 경로(The Most Reliable Quickest Path) 문제는 최단경로문제보다 더 현실적이다[1]. 이 논문에서는 그 중 '가장 신뢰할만한 경로 중에서 가장 빠른 경로' 문제를 자바를 사용하여 시각화함으로써 가변 상황에 따라 다른 경로를 찾아주는 과정을 보여준다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.47-56
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• 1998

This paper describes a moving path generation method for the Autonomous vehicles (AV) to search for paths in an unknown environment by using fixed obstacle information. Algorithms for the AV which were recently proposed have some problems, so it was difficult to utilize these algorithms in the real world. The purpose of this research is to examine the applicability of real-time control and efficient improvement by reducing calculation iterations. In the network which is constructed by the cell-decomposition method, a gate is installed in each cell. By verifying the possibility of gate pass-over, the number of cells which should be considered to find the solution can be reduce. Therefore, algorithm iterations can be dramatically improved. In this paper we have proven that path-generated algorithms are efficient by using simulation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.9-15
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• 2009

Ant Colony System is new meta heuristic for hard combinatorial optimization problem. The original ant colony system accomplishes a pheromone updating about only the global optimal path using global updating rule. But, If the global optimal path is not searched until the end condition is satisfied, only pheromone evaporation happens to no matter how a lot of iteration accomplishment. In this paper, the length of the global optimal path does not improved within the limited iterations, we evaluates this state that fall into the local optimum and selects the next node using changed parameters in the state transition rule. This method has effectiveness of the search for a path through diversifications is enhanced by decreasing the value of parameter of the state transition rules for the select of next node, and escape from the local optima is possible. Finally, the performance of Best and Average_Best of proposed algorithm outperforms original ACS.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.605-614
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• 2007

As a standard query language to search XML data, XQuery and XPath were proposed by W3C. By widely using XQuery and XPath languages, recent researches focus on the development of query processing algorithm and data structure for efficiently processing XML query with the enormous XML database system. Recently, when processing XML path expressions, the concept of the structural join which may determine the structural relationship between XML elements, e.g., ancestor-descendant or parent-child, has been one of the dominant XPath processing mechanisms. However, structural joins which frequently occur in XPath query processing require high cost. In this paper, we propose a new structural join algorithm, called SISJ, based on our structured index, called SI, in order to process XPath queries efficiently. Experimental results show that our algorithm performs marginally better than previous ones. However, in the case of high recursive documents, it performed more than 30% by the pruning feature of the proposed method.