• 제목/요약/키워드: Ant colony optimization algorithm

검색결과 102건 처리시간 0.027초

규칙적인 NoC 구조에서의 네트워크 지연 시간 최소화를 위한 어플리케이션 코어 매핑 방법 연구 (Application Core Mapping to Minimize the Network Latency on Regular NoC Architectures)

  • 안진호;김홍식;김현진;박영호;강성호
    • 대한전자공학회논문지SD
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    • 제45권4호
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    • pp.117-123
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    • 2008
  • 본 논문에서는 규칙적인 형태의 NoC 중 mesh 구조를 기반으로 한 어플리케이션 코어 매핑 알고리즘 연구 내용을 소개한다. 제안된 알고리즘은 ant colony optimization(ACO) 기법을 이용하여 주어진 SoC 내장 코어 및 NoC 특성 정보를 대상으로 가장 효과적인 코어 배치 결과를 도출한다. 설계 목적으로 사용된 네트워크 지연 시간 측정을 위해 평균 흡수 계산 결과를 이용하였으며 제한 조건으로는 NoC 대역폭을 기준으로 하였다. 12개의 코어로 구성되는 실제 기능 블럭을 대상으로 실험한 결과 계산 시간이나 매핑 결과 모두 우수함을 확인할 수 있었다.

A Common Bitmap Block Truncation Coding for Color Images Based on Binary Ant Colony Optimization

  • Li, Zhihong;Jin, Qiang;Chang, Chin-Chen;Liu, Li;Wang, Anhong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권5호
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    • pp.2326-2345
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    • 2016
  • For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding (BTC) of the R, G and B channels. However, common bitmaps generated by some traditional schemes are not the best possible because they do not consider the minimized distortion of the entire color image. In this paper, we propose a near-optimized common bitmap scheme for BTC using Binary Ant Colony Optimization (BACO), producing a BACO-BTC scheme. First, the color image is compressed by the BTC algorithm to get three individual bitmaps, and three pairs of quantization values for the R, G, and B channels. Second, a near-optimized common bitmap is generated with minimized distortion of the entire color image based on the idea of BACO. Finally, the color image is reconstructed easily by the corresponding quantization values according to the common bitmap. The experimental results confirmed that reconstructed image of the proposed scheme has better visual quality and less computational complexity than the referenced schemes.

RRT*를 활용하여 향상된 이종의 개미군집 기반 경로 계획 알고리즘 (Improved Heterogeneous-Ants-Based Path Planner using RRT*)

  • 이준우
    • 로봇학회논문지
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    • 제14권4호
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    • pp.285-292
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    • 2019
  • Path planning is an important problem to solve in robotics and there has been many related studies so far. In the previous research, we proposed the Heterogeneous-Ants-Based Path Planner (HAB-PP) for the global path planning of mobile robots. The conventional path planners using grid map had discrete state transitions that constrain the only movement of an agent to multiples of 45 degrees. The HAB-PP provided the smoother path using the heterogeneous ants unlike the conventional path planners based on Ant Colony Optimization (ACO) algorithm. The planner, however, has the problem that the optimization of the path once found is fast but it takes a lot of time to find the first path to the goal point. Also, the HAB-PP often falls into a local optimum solution. To solve these problems, this paper proposes an improved ant-inspired path planner using the Rapidly-exploring Random Tree-star ($RRT^*$). The key ideas are to use $RRT^*$ as the characteristic of another heterogeneous ant and to share the information for the found path through the pheromone field. The comparative simulations with several scenarios verify the performance of the improved HAB-PP.

Ant colony optimization for dynamic stability of laminated composite plates

  • Shafei, Erfan;Shirzad, Akbar
    • Steel and Composite Structures
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    • 제25권1호
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    • pp.105-116
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    • 2017
  • This paper presents the dynamic stability study of laminated composite plates with different force combinations and aspect ratios. Optimum non-diverging stacking is obtained for certain loading combination and aspect ratio. In addition, the stability force is maximized for a definite operating frequency. A dynamic version of the principle of virtual work for laminated composites is used to obtain force-frequency relation. Since dynamic stiffness governs the divergence or flutter, an efficient optimization method is necessary for the response functional and the relevant constraints. In this way, a model based on the ant colony optimization (ACO) algorithm is proposed to search for the proper stacking. The ACO algorithm is used since it treats with large number of dynamic stability parameters. Governing equations are formulated using classic laminate theory (CLT) and von-Karman plate technique. Load-frequency relations are explicitly obtained for fundamental and secondary flutter modes of simply supported composite plate with arbitrary aspect ratio, stacking and boundary load, which are used in optimization process. Obtained results are compared with the finite element method results for validity and accuracy convince. Results revealed that the optimum stacking with stable dynamic response and maximum critical load is in angle-ply mode with almost near-unidirectional fiber orientations for fundamental flutter mode. In addition, short plates behave better than long plates in combined axial-shear load case regarding stable oscillation. The interaction of uniaxial and shear forces intensifies the instability in long plates than short ones which needs low-angle layup orientations to provide required dynamic stiffness. However, a combination of angle-ply and cross-ply stacking with a near-square aspect ratio is appropriate for the composite plate regarding secondary flutter mode.

Distributed Database Design using Evolutionary Algorithms

  • Tosun, Umut
    • Journal of Communications and Networks
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    • 제16권4호
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    • pp.430-435
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    • 2014
  • The performance of a distributed database system depends particularly on the site-allocation of the fragments. Queries access different fragments among the sites, and an originating site exists for each query. A data allocation algorithm should distribute the fragments to minimize the transfer and settlement costs of executing the query plans. The primary cost for a data allocation algorithm is the cost of the data transmission across the network. The data allocation problem in a distributed database is NP-complete, and scalable evolutionary algorithms were developed to minimize the execution costs of the query plans. In this paper, quadratic assignment problem heuristics were designed and implemented for the data allocation problem. The proposed algorithms find near-optimal solutions for the data allocation problem. In addition to the fast ant colony, robust tabu search, and genetic algorithm solutions to this problem, we propose a fast and scalable hybrid genetic multi-start tabu search algorithm that outperforms the other well-known heuristics in terms of execution time and solution quality.

Intelligent Clustering in Vehicular ad hoc Networks

  • Aadil, Farhan;Khan, Salabat;Bajwa, Khalid Bashir;Khan, Muhammad Fahad;Ali, Asad
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권8호
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    • pp.3512-3528
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    • 2016
  • A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

개미 군집 알고리즘을 이용한 배전계통 재구성 (Reconfiguration of Distribution System using ant colony algorithm)

  • 전영재;김재철;김낙경;최병수
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 A
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    • pp.282-284
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    • 2001
  • This paper presents an efficient algorithm for the loss minimization in distribution systems. Ant colony algorithm is suitable for combinatorial optimization problem as network reconfiguration because it use the long term memory, called pheromone, and heuristic information with the property of the problem. The proposed methodology with some adoptions have been applied to improve the computation time and convergence property. Numerical examples demonstrate the validity and effectiveness of the proposed methodology using 32-bus system.

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방문판매원 문제에 적용한 개선된 개미 군락 시스템 (Improved Ant Colony System for the Traveling Salesman Problem)

  • 김인겸;윤민영
    • 정보처리학회논문지B
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    • 제12B권7호
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    • pp.823-828
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    • 2005
  • 개미 군락 시스템 (Ant Colony System, ACS)은 조합 최적화 문제 중의 하나인 방문 판매원 문제에(Traveling Salesman Problem TSP) 간단하게 응용할 수 있고 좋은 결과를 보여주었다. 그러나 ACS는 작은 TSP에서는 최적해를 구하였지만, 큰 TSP에 대해서는 최적해를 구하지 못하였다. 본 연구에서는 큰 TSP에 대한 최적해를 찾기 위하여 개미들에게 좀 더 많은 정보를 주는 기법의 하나로 한 도시에서 다음 도시로의 탐색 경로를 결정할 때, 인접한 도시들에(adjacent neighbor) 대한 거리로만 결정하지 않고 길이가 w인 부경로(subpath)에 대한 정보를 미리 수집한 후, 이 정보를 이용하여 경로를 생성하였다. 실험 결과 본 연구에서 제안한 기법이 기존의 ACS 기법보다 큰 그래프에서 최적해(Known Optimal)에 가까운 경로를 찾는 것을 볼 수 있다. 그래프에 따라서는 기존의 ACS 기법보다 최대 $70\%$ 이상의 성능이 개선되었으며 평균적으로 $30\%$ 내외의 개선된 결과를 보여준다.

Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

  • Fengjiao Zhou;Rui Ma;Mohamad Shaharudin bin Samsurijan;Xiaoqin Xie
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권4호
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    • pp.903-921
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    • 2024
  • Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

Integrating Ant Colony Clustering Method to a Multi-Robot System Using Mobile Agents

  • Kambayashi, Yasushi;Ugajin, Masataka;Sato, Osamu;Tsujimura, Yasuhiro;Yamachi, Hidemi;Takimoto, Munehiro;Yamamoto, Hisashi
    • Industrial Engineering and Management Systems
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    • 제8권3호
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    • pp.181-193
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    • 2009
  • This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.