• Title/Summary/Keyword: 벌군집 알고리즘

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Selection of controller using improved Artificial Bee Colony algorithm based on Apriori algorithm in SDN environment (SDN 환경에서 Apriori 알고리즘 기반의 향상된 인공벌 군집(ABC) 알고리즘을 이용한 컨트롤러 선택)

  • Yoo, Seung-Eon;Lim, Hwan-Hee;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.01a
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    • pp.39-40
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    • 2019
  • 본 논문에서는 연관규칙 마이닝 알고리즘인 Apriori 알고리즘을 기반으로 향상된 인공벌 군집 알고리즘(ABC algorihtm)을 적용하여 SDN 환경에서 분산된 컨트롤러를 선택하는 모델을 제안하였다. 이를 통해 자주 사용되는 컨트롤러를 우선적으로 선택함으로써 향상된 컨트롤러 선택을 목표로 한다.

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Improved Artificial Bee Clustering (ABC) Algorithm for Solving Consistency Problems in SDN Distributed Controllers (SDN 분산 컨트롤러에서 일관성 문제 해결을 위한 향상된 인공벌 군집(ABC) 알고리즘)

  • Yoo, Seung-Eon;Lym, Hwan-Hee;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2018.07a
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    • pp.145-146
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    • 2018
  • 중앙 집중적인 단일 컨트롤러를 이용할 경우 메시지 과부하로 인해 응답이 지연될 수 있으므로 스위치들이 기존의 컨트롤러를 대신하여 새로운 컨트롤러와 연결되어 트래픽을 처리하는 다중 컨트롤러가 효율적이다. 본 논문에서는 SDN 분산 컨트롤러에서 일관성 문제를 해결하기 위해 우선순위에 기반을 둔 향상된 인공벌 군집(ABC) 알고리즘을 제안한다.

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Design of RBFNN-Based Pattern Classifier for the Classification of Precipitation/Non-Precipitation Cases (강수/비강수 사례 분류를 위한 RBFNN 기반 패턴분류기 설계)

  • Choi, Woo-Yong;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.6
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    • pp.586-591
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    • 2014
  • In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

A Hybrid Search Method Based on the Artificial Bee Colony Algorithm (인공벌 군집 알고리즘을 기반으로 한 복합탐색법)

  • Lee, Su-Hang;Kim, Il-Hyun;Kim, Yong-Ho;Han, Seog-Young
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.23 no.3
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    • pp.213-217
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    • 2014
  • A hybrid search method based on the artificial bee colony algorithm (ABCA) with harmony search (HS) is suggested for finding a global solution in the field of optimization. Three cases of the suggested algorithm were examined for improving the accuracy and convergence rate. The results showed that the case in which the harmony search was implemented with the onlooker phase in ABCA was the best among the three cases. Although the total computation time of the best case is a little bit longer than the original ABCA under the prescribed conditions, the global solution improved and the convergence rate was slightly faster than those of the ABCA. It is concluded that the suggested algorithm improves the accuracy and convergence rate, and it is expected that it can effectively be applied to optimization problems with many design variables and local solutions.