Modified AntNet Algorithm for Network Routing

네트워크 라우팅을 위한 개선된 AntNet 알고리즘

  • 강득희 (전북대학교 전자정보공학부) ;
  • 이말례 (전북대학교 공업기술연구소 컴퓨터공학과)
  • Published : 2009.05.15

Abstract

During periods of large data transmission, routing selection methods are used to efficiently manage data traffic and improve the speed of transmission. One approach in routing selection is AntNet that applies the Ant algorithm in transmissions with uniform probability. However, this approach uses random selection, which can cause excessive data transmission rates and fail to optimize data This paper presents the use of the Genetic Algorithm (GA) to efficiently route and disperse data transmissions, during periods with "unnecessary weight increases for random selection". This new algorithm for improved performance provides highly accurate estimates of the transmission time and the transmission error rate.

다량의 데이터를 전송할 때, 시간 단축과 효율적인 트래픽관리를 위해 네트워크의 라우팅 선택 방법이 사용되고 있다. Ant 알고리듬을 적용한 AntNet은 라우팅 선택 확률이 동일할 때, 랜던선택을 한다. 그로인해서 불필요한 가중치가 발생하여 트래픽이 증가한다. 본 논문은 이를 해결하기 위해 GA 알고리듬을 AntNet에 적합하여 데이터 전송을 위한 전송시간 감소와 효율적인 트래픽 분산을 해결하였다. 제안한 알고리즘 성능평가를 위해서 본 논문에서는 대량의 데이터를 전송하기 위한 경로를 설정하고, 전송시간과 전송 오류율을 평가하여 우수성을 보였다.

Keywords

References

  1. L. Bianchi, L. M. Gambardella, and M. Dorigo. An ant colony optimization approach to the probabilistic traveling salesman problem. In Proceedings of PPSN-VII, Seventh International Conference on Parallel Problem Solving from Nature, volume 2439 of Lecture Notes in Computer Science. Springer Verlag, Berlin, Germany, 2002
  2. B. Baran and R. Sosa. A new approach for AntNet routing. In Proceedings of the 9th International Conference on Computer Communications Networks, Las Vegas, USA, 2000
  3. G. Di Caro and M. Dorigo. Two ant colony algorithms for best-effort routing in datagram networks. In Proceedings of the Tenth IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS'98), pages 541. 546. IASTED/ACTA Press, 1998
  4. G. Di Caro. A society of ant-like agents for adaptive routing in networks. DEA thesis in Applied Sciences, Polytechnic School, Universit´e Libre de Bruxelles, Brussels (Belgium), May 2001
  5. A.V. Vasilakos and G.A. Papadimitriou. A new approach to the design of reinforcement scheme for learning automata: Stochastic Estimator Learning Algorithms. Neurocomputing, 7(275), 1995
  6. G. Di Caro and T. Vasilakos. Ant-SELA: Antagents and stochastic automata learn adaptive routing tables for QoS routing in ATM networks. ANTS'2000 -From Ant Colonies to Articial Ants: Second International Workshop on Ant Colony Optimization, Brussels (Belgium), September 8-9, 2000
  7. Yongkyu Park. 'A Genetic Approach for Routing Problems in Optical Meshes,' Korea IITA. 1999