• Title/Summary/Keyword: 경로 최적화

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A Query Pruning Technique for Optimizing Regular Path Expressions in Semistructured Databases (준구조적 데이타베이스에서의 정규경로표현 최적화를 위한 질의전지 기법)

  • Park, Chang-Won;Jeong, Jin-Wan
    • Journal of KIISE:Databases
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    • v.29 no.3
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    • pp.217-229
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    • 2002
  • Regular path expressions are primary elements for formulating queries over the semistructured data that does not assume the conventional schemas. In addition, the query pruning is an important optimization technique to avoid useless traversals in evaluating regular path expressions. However, the existing query pruning often fails to fully optimize multiple regular path expressions, and the previous methods that post-process the result of the existing query pruning must check exponential combinations of sub-results. In this paper, we present a new query pruning technique that consists of the preprocessing phase and the pruning phase. Our two-phase query pruning is affective in optimizing multiple regular path expressions, and is more scalable than the previous methods in that it never check the exponential combinations of sub-results.

An Ant Colony Optimization Heuristic to solve the VRP with Time Window (차량 경로 스케줄링 문제 해결을 위한 개미 군집 최적화 휴리스틱)

  • Hong, Myung-Duk;Yu, Young-Hoon;Jo, Geun-Sik
    • The KIPS Transactions:PartB
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    • v.17B no.5
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    • pp.389-398
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    • 2010
  • The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.

The Study on the Optimized Earthwork Transfer Path Algorithm Considering the Precluded Area of Massive Cutting and Banking (대규모 절성토 지역의 제척지를 고려한 최적화된 토량이동 경로 알고리즘 개발에 관한 연구)

  • Kang, Tae-Wook;Cho, Yoon-Ho
    • International Journal of Highway Engineering
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    • v.13 no.4
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    • pp.1-8
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    • 2011
  • The purpose of this study is to suggest the optimized transfer algorithm of earthwork considering the precluded area such as the lake, bogs. The earthwork transfer plan in massive cutting and banking should be established because of affecting the construction cost highly. Until now, there was the study about the optimized earthwork transfer model considering the OR(Operating Research). but isn't the study about the model considering the precluded area such as the lake, bogs. In most cases, the engineer adjusts the earthwork transfer path considering the precluded area, manually. The presented model suggests to calculate various visibility paths with $A^*$algorithm after converting the precluded area to polygon topology. By using this paths, the minimum cost path to optimize the earthwork transfer can be obtained. In this study, the validity of the model was proved as implementing the system for the optimized earthwork transfer considering the precluded area.

Balance between Intensification and Diversification in Ant Colony Optimization (개미 집단 최적화에서 강화와 다양화의 조화)

  • Lee, Seung-Gwan;Choi, Jin-Hyuk
    • 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.

A Optimization Study of UAV Path Planning Generation based-on Rapid-exploring Random Tree Method (급속탐색랜덤트리기법 기반의 무인 비행체 경로계획생성 최적화 연구)

  • Jae-Hwan Bong;Seong-Kyun Jeong
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.5
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    • pp.981-988
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    • 2023
  • As the usage of unmanned aerial vehicles expands, the development and the demand of related technologies are increasing. As the frequency of operation increases and the convenience of operation is emphasized, the importance of related autonomous flight technology is also highlighted. Establishing a path plan to reach the destination in autonomous flight of an unmanned aerial vehicle is important in guidance and control, and a technology for automatically generating path plan is required in order to maximize the effect of unmanned aerial vehicle. In this study, the optimization research of path planning using rapid-exploring random tree method was performed for increasing the effectiveness of autonomous operation. The path planning optimization method considering the characteristics of the unmanned aerial vehicle is proposed. In order to achieve indexes such as optimal distance, shortest time, and passage of mission points, the path planning was optimized in consideration of the mission goals and dynamic characteristics of the unmanned aerial vehicle. The proposed methods confirmed their applicability to the generation of path planning for unmanned aerial vehicles through performance verification for obstacle situations.

Vehicle Crash Simulation using Trajectory Optimization (경로 최적화 알고리즘을 이용한 3차원 차량 충돌 시뮬레이션)

  • Seong, Jin-Wook;Ko, Seung-Wook;Kwon, Tae-Soo
    • Journal of the Korea Computer Graphics Society
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    • v.21 no.5
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    • pp.11-19
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    • 2015
  • Our research introduces a novel system for creating 3D vehicle animation. Our system is for intuitively authoring vehicle accident scenes according to videos or based on user-drawn trajectories. Our system has been implemented by combining three existing ideas. The first part is for obtaining 3D trajectory of a vehicle from black-box videos. The second part is a tracking algorithm that controls a vehicle to follow a given trajectory with small errors. The last part optimizes the vehicle control parameters so that the error between the input trajectory and simulated vehicle trajectory is minimized. We also simulate the deformation of the car due to an impact to achieve believable results in real-time.

A Study on the Optimization of packing Step of Injection Molding Process (사출성형공정 중 보압과정의 최적화 연구)

  • 이승종
    • The Korean Journal of Rheology
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    • v.10 no.2
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    • pp.113-120
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    • 1998
  • 사출성형공정은 대표적인 고분자 가공공정으로 그 복잡한 특성으로 인하여 공정변 수를 최적화하는 것을 주로 경험에 의존해 왔다. 본 연구에서는 사출성형공정의 보압과정 중에 보압의 이력을 최적화하여 제품각 부분의 부피수축율차이를 최소가 되게 하는 최적화 시스템을 개발하였다. 최적화 알고리즘으로는 GA방법을 사용하였으며 본 연구에서 제안한 최적화 시스템으로 보압과정의 최적화를 수행한 결과 부피수축율의 차이가 현저히 감소하는 것을 알수 있었다. 특히 SA방법을 사용하는 경우 초기의 최적화 속도가 GA를 사용하는 경 우에 비해서 뛰어남을 알수 있었다. 또한 충전과정과 보압과정을 함께 최적화하여 보압과정 만 최적화한 결과와 비교하여 보았다.

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Combining A* and Genetic Algorithm for Efficient Path Search (효율적인 경로 탐색을 위한 A*와 유전자 알고리즘의 결합)

  • Kim, Kwang Baek
    • 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.

Route Optimization for Energy-Efficient Path Planning in Smart Factory Autonomous Mobile Robot (스마트 팩토리 모빌리티 에너지 효율을 위한 경로 최적화에 관한 연구)

  • Dong Hui Eom;Dong Wook Cho;Seong Ju Kim;Sang Hyeon Park;Sung Ho Hwang
    • Journal of Drive and Control
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    • v.21 no.1
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    • pp.46-52
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    • 2024
  • The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A*, and RRT*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

Link Assignment in Low-Earth Orbit Satellite Networks using Simulated Annealing (시뮬레이티드 어닐링을 이용한 저궤도 위성망에서의 링크할당)

  • Jang, Heung-Seong;Kim, Byeong-Wan;Lee, Chang-Geon;Min, Sang-Ryeol;Choe, Yang-Hui;Yang, Hyeon;Kim, Deok-Nyeon;Kim, Jong-Sang
    • Journal of KIISE:Computer Systems and Theory
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    • v.26 no.2
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    • pp.211-222
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    • 1999
  • 본 논문은 위성간 링크를 이용하여 저궤도 위성망을 구성할 때 발생하는 링크할당 문제를 효율적으로 풀기 위한 기법을 제안한다. 제안된 기법은 먼저 위성 궤도운동의 주기성에 기반하여 저궤도 위성망을 유한상태기계로 모델링한 후에, 유한상태기계의 각 상태에서 최적의 링크할당을 구하기 위해서 조합형 최적화 문제에 많이 쓰이는 시뮬레이팅드 어닐리을 이용한다. 제안된 기법의 이점은 저궤도 위성망을 유한상태기계로 모델링함으로써 도적인 움직임을 보이는 저궤도 위성망에서의 링크할당 문제를 고정된 위상을 가지는 망에서의 링크할당 문제로 단순화시키고 이를 토대로 최적화기법을 적용할 수 있다는 것이다. 시뮬레이티드 어닐링에 의하여 최적화된 링크할당의 성능은 정규링크할당과의 비교.분석을 통해서 평가된다. 최적화된 링크할당과 정규링크할당의 성능분석을 위하여 정적경로배정과 동적경로배정 기법이 고려된다. 시뮬레이션을 통한 실험결과는 정적경로배정을 적용한 최적링크할당 기법이 호 봉쇄확률 측면에서 최고의 성능을 가짐을 보여준다. 링크할당기법이 같은 경우에는 정적경로배정이 동적경로배정보다 우수한 성능을 보이는데 이는 동적경로배정의 경우에 상태전이 후에 경로배정 표가 안정화되기 위해서 많은 시간을 필요로 하기 때문이다. 본 논문에서는 제안된 링크할당 기법은 작은 용량의 위성간 링크를 가지고서 많은 호에대한 서비스를 제공하고자 할 때 유용하며, 호의 서비스를 위하여 실제로 필요한 위성간 링크의 용량은 실험결과로부터 유추될 수 있다.