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휴리스틱에 의하여 개선된 반딧불이 알고리즘의 설계와 분석

A Design and Analysis of Improved Firefly Algorithm Based on the Heuristic

  • 이현숙 (동양미래대학 전산정보학부) ;
  • 이정우 (서강대학교 컴퓨터공학과) ;
  • 오경환 (서강대학교 컴퓨터공학과)
  • 투고 : 2011.01.10
  • 심사 : 2011.01.17
  • 발행 : 2011.02.28

초록

본 논문에서는 최근 Xin-She Yang에 의해 소개된 반딧불이 알고리즘(FA)에 휴리스틱을 적용하여 개선하는 방안을 제안한다. 또한 이를 위하여 기존의 FA를 이와 유사한 문제영역의 알고리즘인 Particle Swarm Optimization(PSO)와 정확도 측면, 수렴 시간 측면, 각 입자의 움직임 측면에서 비교 분석한다. 비교 실험 결과, FA의 정확도는 PSO보다 나쁘지 않았지만, 수렴 속도는 느린 것으로 나타났다. 본 논문은 이에 대한 직관적인 원인을 고찰하고, 이를 극복하기 위해, 기존의 FA에 부분 돌연변이 휴리스틱을 적용하여 개선된 FA(Improved FA)를 제안한다. 벤치마크 함수들을 최적화 하는 비교 실험 결과, 개선된 FA가 PSO와 기존의 FA보다 정확도와 수렴속도 측면에서 우수함을 보이고자 한다.

In this paper, we propose a method to improve the Firefly Algorithm(FA) introduced by Xin-She Yang, recently. We design and analyze the improved firefly algorithm based on the heuristic. We compare the FA with the Particle Swarm Optimization (PSO) which the problem domain is similar with the FA in terms of accuracy, algorithm convergence time, the motion of each particle. The compare experiments show that the accuracy of FA is not worse than PSO's, but the convergence time of FA is slower than PSO's. In this paper, we consider intuitive reasons of slow convergence time problem of FA, and propose the improved version of FA using a partial mutation heuristic based on the consideration. The experiments using benchmark functions show the accuracy and convergence time of the improved FA are better than them of PSO and original FA.

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참고문헌

  1. G. Bekey and J. Yuh, “The status of robotics,” IEEE Robotics and Automation Magazine, Vol.15, No.1, pp.80-86, 2008 https://doi.org/10.1109/M-RA.2007.907356
  2. A. Howard, M. Matari'c, and G. Sukhatme., “Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem,” the 6th International Symposium on Distributed Autonomous Robotics Systems (DARS02), 2002
  3. M. Gerke, “Genetic path planning for mobile robots”, American Control Conference, 1999.
  4. Q, Zhang, G. Gu, “Path planning based on Improved binary 입자 swarm optimization Algorithm”, Robotics, Automation and Mechatronics, 2008 IEEE Conference on, pp.462-466
  5. T. Arei, J. Ota, “Motion Planning of Multiple Mobile Robots”, Proc. IEEE International Conference on Intelligent Robots and Automation, pp.1761-1768, 1992.
  6. A. L. Christensen, “From fireflies to fault-tolerant swarms of robots”, Evolutionary Computation, IEEE Transactions on Vol.13, pp.754-766, 2009 https://doi.org/10.1109/TEVC.2009.2017516
  7. A. Schworer, P. Hovey, “Newton-Raphson Versus Fisher Scoring Algorithm in Calculating Maximum Likelihood Estimates”, Electronic Proceedings of Undergraduate Mathematics Day, No.1, 2004.
  8. A. E. Eiben, J. E. Smith, “Introduction to Evolutionary Computing”, Springer-Verlog, New York, 2003.
  9. X.-S. Yang, “Firefly algorithms for multimodal optimization”, Stochastic Algorithms: Foundations and Applications, Lecture Notes in Computer Sciences, Vol. 5792, pp. 169-178, 2009. https://doi.org/10.1007/978-3-642-04944-6_14
  10. J. Kennedy, R. C. Eberhart, “입자 Swarm Optimization”, IEEE International Conference on Neural Network, Vol.4, pp.1942-1948, 1995 https://doi.org/10.1109/ICNN.1995.488968
  11. Y. Shi, R. C. Eberhart, “A modified 입자 swarm optimizer”, Proceedings of the IEEE International Conference on Evolutionary Computation, pp.69-73, 1998