• Title/Summary/Keyword: Traveling Salesman Problem (TSP)

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The Bisection Seed Detection Heuristic for Solving the Capacitated Vehicle Routing Problem (한정 용량 차량 경로 탐색 문제에서 이분 시드 검출 법에 의한 발견적 해법)

  • Ko, Jun-Taek;Yu, Young-Hoon;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.15 no.1
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    • pp.1-14
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    • 2009
  • The Capacitated Vehicle Routing Problem (CVRP) is the problem that the vehicles stationed at central depot are to be optimally routed to supply customers with demands, satisfying vehicle capacity constraints. The CVRP is the NP-hard as it is a natural generalization of the Traveling Salesman Problem (TSP). In this article, we propose the heuristic algorithm, called the bisection seed detection method, to solve the CVRP. The algorithm is composed of 3-phases. In the first phase, we work out the initial cluster using the improved sweep algorithm. In the next phase, we choose a seed node in each initial cluster by using the bisection seed detection method, and we compose the rout with the nearest node from each seed. At this phase, we compute the regret value to decide the list of priorities for the node assignment. In the final phase, we improve the route result by using the tabu search and exchange algorithm. We compared our heuristic with different heuristics such as the Clark-Wright heuristic and the genetic algorithm. The result of proposed heuristic show that our algorithm can get the nearest optimal value within the shortest execution time comparatively.

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A Hybrid Genetic Algorithm Using Epistasis Information for Sequential Ordering Problems (서열순서화문제를 위한 상위정보를 이용하는 혼합형 유전 알고리즘)

  • Seo Dong-Il;Moon Byung-Ro
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.6
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    • pp.661-667
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    • 2005
  • In this paper, we propose a new hybrid genetic algorithm for sequential ordering problem (SOP). In the proposed genetic algorithm, the Voronoi quantized crossover (VQX) is used as a crossover operator and the path-preserving 3-Opt is used as a local search heuristic. VQX is a crossotver operator that uses the epistasis information of given problem instance. Since it is a crossover proposed originally for the traveling salesman problem (TSP), its application to SOP requires considerable modification. In this study, we appropriately modify VQX for SOP, and develop three algorithms, required in the modified VQX, named Feasible solution Generation Algorithm, Precedence Cycle Decomposition Algorithm, and Genic Distance Assignment Method. The results of the tests on SOP instances obtained from TSPLIB and ZIB-MP-Testdata show that the proposed genetic algorithm outperforms other genetic algorithms in stability and solution quality.

UAV LRU Layout Optimizing Using Genetic Algorithm (유전알고리즘을 이용한 무인항공기 장비 배치 최적 설계)

  • Back, Sunwoo
    • 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.

Development of Mission Analysis and Design Tool for ISR UAV Mission Planning (UAV 감시정보정찰 임무분석 및 설계 도구 개발)

  • Kim, Hongrae;Jeon, Byung-Il;Lee, Narae;Choi, Seong-Dong;Chang, Young-Keun
    • 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.

Optimal Teaching for a Spot Welding Robot Using CAD Data (CAD 데이타를 이용한 용접용 로보트의 최적 교시)

  • Yi, Soo-Yeong;Chung, Myung-Jin;Bien, Zeung-Nam
    • 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.

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Optimal Scheduling of Satellite Tracking Antenna of GNSS System (다중위성 추적 안테나의 위성추적 최적 스케쥴링)

  • Ahn, Chae-Ik;Shin, Ho-Hyun;Kim, You-Dan;Jung, Seong-Kyun;Lee, Sang-Uk;Kim, Jae-Hoon
    • 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.