• Journal of the Korea Computer Industry Society
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• v.3 no.2
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• pp.185-190
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• 2002

As the TSP(Traveling Salesman Problem) belongs to the class of NP-complete problems, various techniques are required for finding optimum or near optimum solution to the TSP. This paper designs a distributed genetic algorithm in order to reduce the execution time and obtain more near optimal using multi-slave model for the TSP. Especially, distributed genetic algorithms with multiple populations are difficult to configure because they are controlled by many parameters that affect their efficiency and accuracy. Among other things, one must decide the number and the size of the populations (demes), the rate of migration, the frequency of migrations, and the destination of the migrants. In this paper, I develop random dynamic migration rate that controls the size and the frequency of migrations. In addition to this, I design new migration policy that selects the destination of the migrants among the slaves

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.788-793
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• 2015

This paper proposes a path planning algorithm to reduce the inspection time of AOI (Automatic Optical Inspection) machines for SMT (Surface Mount Technology) in-line system. Since the field-of-view of the camera attached at the machine is much less than the entire inspection region of board, the inspection region should be clustered to many groups. The image acquisition time depends on the number of groups, and camera moving time depends on the sequence of visiting the groups. The acquired image is processed while the camera moves to the next position, but it may be delayed if the group includes many components to be inspected. The inspection delay has influence on the overall job time of the machine. In this paper, we newly considers the inspection delay time for path planning of the inspection machine. The unified approach using genetic algorithm is applied to generates the groups and visiting sequence simultaneously. The chromosome, crossover operator, and mutation operator is proposed to develop the genetic algorithm. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.133-145
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• 2010

Recently, due to advances in information technologies, the trend of tour types has been changing from package tour to independent tour. Independent tour is a tour which a traveler collect airplane ticket, travel destinations, sightseeing time, transport, lodging and plan traveling schedules by oneself. But the traveler has many difficulties for predicting tour schedules, due to lack of adequate information of travel destinations. In this study, traveling schedule prediction method which to minimize the cumulative fatigue of tourist for use of unnecessary transport is proposed using travelling salesman problem algorithm. It is considered moving time between sightseeing, sightseeing time on destination and traveling time for a day.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.181-190
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• 2014

The optimized flight path planning which is appropriate for UAV operation with high performance and multiplex sensors is required for efficient ISR missions. Furthermore, a mission visualization tool is necessary for the assessment of MoE(Measures of Effectiveness) prior to mission operation and the urgent tactical decision in peace time and wartime. A mission visualization and analysis tool was developed by combining STK and MATLAB, whose tool was used for UAV ISR mission analyses in this study. In this mission analysis tool, obstacle avoidance and FoM(Figure of Merit) analysis algorithms were applied to enable the optimized mission planning.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.24-33
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• 1990

Since a number of welding points are distributed in an automobile part, the number of welding points alloted to each robot are large. So, there is an increasing need of an optimal sequence planning to minimize the total welding time. In this paper, an off-line programming scheme for effective teaching of a spot welding robot is presented. A collision free, optimal welding sequence planning is done through applying the modified Traveling Salesman Problem algorithm. Also, a data extraction method from an existing general CAD system is presented for reuse of the existing exact model data produced by a model designer and easy constructing the world model data base. The result show that the proposed system could enhance the efficiency of spot welding robot in automobile industry.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.666-673
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• 2008

To construct the accurate radio satellite navigation system, the efficient communication each satellite with the ground station is very important. Throughout the communication, the orbit of each satellite can be corrected, and those information will be used to analyze the satellite satus by the operator. Since there are limited resources of ground station, the schedule of antenna's azimuth and elevation angle should be optimized. On the other hand, the satellite in the medium earth orbit does not pass the same point of the earth surface due to the rotation of the earth. Therefore, the antenna pass schedule must be updated at the proper moment. In this study, Q learning approach which is a form of model-free reinforcement learning and genetic algorithm are considered to find the optimal antenna schedule. To verify the optimality of the solution, numerical simulations are conducted.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.100-107
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• 2011

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. In this paper, we deal with the performance improvement techniques through balance the intensification and diversification in Ant Colony System(ACS) which is one of Ant Colony Optimization(ACO). In this paper, we propose the hybrid searching method between intensification strategy and diversification strategy. First, 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. And then we consider the overlapping edge of the global best path of the previous and the current, and, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath, ACS-Iter and ACS-Global-Ovelap algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.189-196
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• 2010

Spatial information and smart phone technology have made innovative improvement of daily life. Spatial and geographic information are in practice for various applications. Especially, spatial information along with information and telecommunication technology could create new contents for providing services for convenient daily life. Spatial information technology, recently, is not only for acquiring location and attribute data but also providing tools to extract information and knowledge systematically for decision making. Various indoor applications have emerged in accordance with demands on daily GIS(Geographic information system). This paper aims for applying spatial information technology to support decision-making in shopping. The main contents include product database, optimal path search, shopping time expectation, automatic housekeeping book generation and analysis. Especially for foods, function to analyze information of the nutrition facts could help to improve dietary pattern and well-being. In addition, this system is expected to provide information for preventing overconsumption and impulse purchase could help economical and effective purchase pattern by analyzing propensity to consume.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1217-1228
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• 2005

Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.