• Title/Summary/Keyword: k개의 가장 가까운 이웃 에이전트

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An Improved Algorithm of Searching Neighbor Agents in a Large Flocking Behavior (대규모 무리 짓기에서 이웃 에이전트 탐색의 개선된 알고리즘)

  • Lee, Jae-Moon;Jung, In-Hwan
    • Journal of Korea Multimedia Society
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    • v.13 no.5
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    • pp.763-770
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    • 2010
  • This paper proposes an algorithm to enhance the performance of the spatial partitioning method for a flocking behavior. One of the characteristics in a flocking behavior is that two agents may share many common neighbors if they are spatially close to each other. This paper improves the spatial partitioning method by applying this characteristic. While the conventional spatial partitioning method computes the k-nearest neighbors of an agent one by one, the proposed method computes simultaneously the k-nearest neighbors of agents if they are spatially close to each other. The proposed algorithm was implemented and its performance was experimentally compared with the original spatial partitioning method. The results of the comparison showed that the proposed algorithm outperformed the original method by about 33% in average.

An Improvement of Finding Neighbors in Flocking Behaviors by Using a Simple Heuristic (단순한 휴리스틱을 사용하여 무리 짓기에서 이웃 에이전트 탐색방법의 성능 개선)

  • Jiang, Zi Shun;Lee, Jae-Moon
    • Journal of Korea Game Society
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    • v.11 no.5
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    • pp.23-30
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    • 2011
  • Flocking behaviors are frequently used in games and computer graphics for realistic simulation of massive crowds. Since simulation of massive crowds in real time is a computationally intensive task, there were many researches on efficient algorithm. In this paper, we find experimentally the fact that there are unnecessary computations in the previous efficient flocking algorithm, and propose a noble algorithm that overcomes the weakness of the previous algorithm with a simple heuristic. A number of experiments were conducted to evaluate the performance of the proposed algorithm. The experimental results showed that the proposed algorithm outperformed the previous efficient algorithm by about 21% on average.