• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.488-496
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• 2015

This paper proposes a framework to determine the routes of multiple unmanned aerial vehicles (UAVs) to conduct multiple tasks in different locations considering the survivability of the vehicles. The routing problem can be formulated as the vehicle routing problem (VRP) with different cost matrices representing the trade-off between the safety of the UAVs and the mission completion time. The threat level for a UAV at a certain location was modeled considering the detection probability and the shoot-down probability. The minimal-cost path connecting two locations considering the threat level and the flight distance was obtained using the Dijkstra algorithm in hexagonal cells. A case study for determining the optimal routes for a persistent multi-UAVs surveillance and reconnaissance missions given multiple enemy bases was conducted and its results were discussed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.8
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• pp.621-629
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• 2020

LRU layout is a complex problem that requires consideration of various criteria such as airworthiness, performance, maintainability and environmental requirements. As aircraft functions become more complex, the necessary equipment is increasing, and unmanned aerial vehicles are equipped with more equipment as a substitute for pilots. Due to the complexity of the problem, the increase in the number of equipment, and the limited development period, the placement of equipment is largely dependent on the engineer's insight and experience. For optimization, quantitative criteria are required for evaluation, but criteria such as safety, performance, and maintainability are difficult to quantitatively compare or have limitations. In this study, we consider the installation and maintenance of the equipment, simplify the deployment model to the traveling salesman problem, Optimization was performed using a genetic algorithm to minimize the weight of the connecting cable between the equipment. When the optimization results were compared with the global calculations, the same results were obtained with less time required, and the improvement was compared with the heuristic.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.147-152
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• 2008

Ant System(AS) 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, AS is applied to the Multicast Routing Problem. Multicast Routing is modeled as the NP-complete Steiner tree problem. This is the shortest path from source node to all destination nodes. We proposed new AS to resolve this problem. The proposed method selects the neighborhood node to consider all costs of the edge and the next node in state transition rule. Also, The edges which are selected elite agents are updated to additional pheromone. Simulation results of our proposed method show fast convergence and give lower total cost than original AS and $AS_{elite}$.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.99-106
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• 2017

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1318-1324
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• 2007

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.