• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.147-150
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• 2008

The real road network is regarded as a grid, and the grid is divided by fixed-sized cells. The path-finding is composed of two step searching. First searching travels on the abstract graph which is composed of a set of psuedo vertexes and a set of psuedo edges that are created by real road network and fixed-sized cells. The result of the first searching is a psuedo path which is composed of a set of selected psuedo edges. The cells intersected with the psuedo path are called as valid cells. The second searching travels with $A^*$ algorithm on valid cells. As pruning search space by removing the invalid cells, it would be possible to reduce the cost of exploring on real road network. In this paper, we present the method of creating the abstract graph and propose a path-finding algorithm on the abstract graph for extracting search space before traveling on real road network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.943-948
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• 2018

In this paper, we propose a hybrid approach of combining $A^*$ and Genetic algorithm in the path search problem. In $A^*$, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in $A^*$ algorithm and the result verifies the superiority of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2343-2348
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• 2008

Ant algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the improved $AS_{rank}$ algorithms. The original $AS_{rank}$ algorithm accomplishes a pheromone updating about only the paths which will be composed of the optimal path is higher, but, the paths which will be composed the optimal path is lower does not considered. In this paper, The proposed method evaporate the pheromone of the paths which will be composed of the optimal path is lowest(worst tour path), it is reducing the probability of the edges selection during next search cycle. Simulation results of proposed method show lower average search time and average iteration than original ACS.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.69-74
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• 2011

In this paper, we propose a foot-and-mouth disease prevention system using the RTLS technology and $A^*$ algorithm-based optimal path search method to avoid foot-and-mouth disease areas. The main features and contributions of the proposed system are as follows. First, the proposed system is developed based on active-tag for identifying status and location information of livestock. Second, the system is newly designed based on $A^*$ algorithm for supporting optimal path search services. The performance evaluation of the proposed system is performed via simulation. The results of performance evaluation show that the proposed system can efficiently support the optimal path search services.

• Journal of Families and Better Life
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• v.11 no.1
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• pp.12-21
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• 1993

This Study focused on consumer information search and consumer satisfaction according to product characteristics and sex. For these purpose a survey was conducted using questionaires on 225 males 299 females that lived in Seoul. Statistics used for data were Frequency. Prequency. Percentile Mean Multiple Regression Analysis and Path Analysis. The major findings were: 1) I male Group reference orientation were effect on convenience experience credience goods and information search were effect on experience credience goods. 2) In Femal Group her job and information search effect on convenience experience credience goods and involvement effect on credience goods only.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.3
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• pp.329-336
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• 2000

A method for automatic extraction of 2-dimensional finite element connectivities by searching the shortest closed path from a certain node to the starting node was developed. Only the best path among the possible paths was probed. The uniqueness and validity of the extracted path were examined. The proposed method was proved to be complete. Examples show that the proposed method can extract elements exactly from the irregular mesh which can not be handled easily by the conventional automatic mesh generation.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.71-77
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• 2004

While doing games, units have to find goal And according to algorism, there is great difference in time and distance. In this paper the researcher compared and described characteristics of each of the improved algorism and A* algorism by giving depth-first search, breadth-first search and distance value and then argued algorism. In addition. by actually calculating the presumed value in A* a1gorism, the researcher finds the most improved value. Finally, by means of comparison between A* algorism and other one, the researcher verified its excellence and did simple path-finding using A* algorism.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.103-114
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• 2008

Overlay Multicast is a promising approach to overcome the implementation problem of IP multicast. Real time services like Internet broadcasting are provided by the overlay multicast technology due to the complex nature and high cost of IP multicast. To reduce frequent updates of multicast members and to support real time service without delay, we suggest a reliable overlay multicast tree based on members' sojourn probabilities. Path reliabilities from a source to member nodes are considered to maximize the reliability of an overlay multicast tree. The problem is formulated as a binary integer programming with degree and delay bounds. A tabu search heuristic is developed to solve the NP-complete problem. Outstanding results are obtained which is comparable to the optimal solution and applicable in real time.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1007-1008
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• 2006

This paper presents a optimal path search of mobile robot using energy efficiency. Proposed method has two goals that determining the velocity of mobile robot and finding the optimal path. We model the relationship of motors' speed and power consumption with quadratic polynomials. In order to verify the effectiveness of the proposed method, we performed simulations on the energy consumption of different paths.