Smart Structures and Systems

  • 제18권3호
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  • Pages.425-448
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  • 2016
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A new swarm intelligent optimization algorithm: Pigeon Colony Algorithm (PCA)

  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Wen, Kai-Fang (School of Civil Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (School of Civil Engineering, Dalian University of Technology)
  • 투고 : 2015.10.15
  • 심사 : 2016.03.05
  • 발행 : 2016.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

참고문헌

  1. Colorni, A., Dorigo, M. and Maniezzo, V. (1991), "A distributed optimization by ant colonies", Proceedings of the 1st European Conference on Artificial Life, Paris, December.
  2. Eberhart, R.C. and Kennedy, J. (1995), "A new optimizer using particle swarm theory", Proceedings of the 6th International Symposium On Micro Machine and Human Science, NJ.
  3. Elbeltagi, E., Hegazy, T. and Grierson, D. (2005), "Comparison among five evolutionary-based optimization algorithms", Adv. Eng. Inform., 19(1), 43-53. https://doi.org/10.1016/j.aei.2005.01.004
  4. Eusuff, M., Lansey, K. and Pasha, F. (2006), "Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization", Eng. Optimiz., 38(2), 129-154. https://doi.org/10.1080/03052150500384759
  5. Eusuff, M.M. and Lansey, K.E. (2003), "Optimization of water distribution network design using the shuffled frog leaping algorithm", J. Water Res. Pl.-ASCE, 129(3), 210-225. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210)
  6. Geem, Z.W., Kim, J.H. and Loganathan, G.V. (2001), "A new heuristic optimization algorithm: harmony search", Simulat., 76(2), 60-68. https://doi.org/10.1177/003754970107600201
  7. Holland, J.H. (1975), Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, Michigan, USA.
  8. Holland, J.H. (1988), "Genetic algorithms and machine learning", Mach. Learn., 3(2), 95-99. https://doi.org/10.1023/A:1022602019183
  9. Kennedy, J. (2010), "Encyclopedia of machine learning", Springer, Berlin, Germany.
  10. Lawler, E.L. and Wood, D.E. (1966), "Branch-and-bound methods: A survey", Oper. Res., 14(4), 699-719. https://doi.org/10.1287/opre.14.4.699
  11. Lei, Y., Liu, C., Jiang Y.Q., and Mao, Y.K. (2013), "Substructure based structural damage detection with limited input and output measurements", Smart. Struct. Syst., 12(6), 619-640. https://doi.org/10.12989/sss.2013.12.6.619
  12. Lei, Y., Wang, H.F. and Shen, W.A. (2012), "Update the finite element model of Canton Tower based on direct matrix updating with incomplete modal data", Smart. Struct. Syst., 10(4-5), 471-483. https://doi.org/10.12989/sss.2012.10.4_5.471
  13. Li, J. and Law, S.S. (2012), "Damage identification of a target substructure with moving load excitation", Mech. Syst. Signal Pr., 30(7), 78-90. https://doi.org/10.1016/j.ymssp.2012.02.002
  14. Li, J., Hao, H. and Lo, J.V. (2015), "Structural damage identification with power spectral density transmissibility: numerical and experimental studies", Smart. Struct. Syst., 15(1), 15-40. https://doi.org/10.12989/sss.2015.15.1.015
  15. Li, X.L. and Qian, J.X. (2003), "Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques", Circ. Syst., 1, 1-6.
  16. Li, X.L., Lu, F., Tian, G.H. and Qian, J.X. (2004), "Applications of artificial fish school algorithm in combinatorial optimization problems", J. Shandong Univ. (Eng. Sci.), 5, 015.
  17. MATLAB, The MathWorks, Inc. Natwick, MA (USA), http://www.mathworks.com.
  18. Moller, M.F. (1993), "A scaled conjugate gradient algorithm for fast supervised learning", Neur. Net., 6(4), 525-533. https://doi.org/10.1016/S0893-6080(05)80056-5
  19. Nagy, M., A kos, Z., Biro, D. and Vicsek, T. (2010), "Hierarchical group dynamics in pigeon flocks", Nature, 464(7290), 890-893. https://doi.org/10.1038/nature08891
  20. Perera, T.B.D. and Guilford, T. (1999), "The orientational consequences of flocking behavior in homing pigeons, Columba livia", Ethology, 105(1), 13-23. https://doi.org/10.1111/j.1439-0310.1999.tb01217.x
  21. Qi, L. and Sun, J.A. (1993), "A nonsmooth version of Newton's method", Math. Program., 58(1-3), 353-367. https://doi.org/10.1007/BF01581275
  22. Shi, Y. and Eberhart, R. (1998), "A modified particle swarm optimizer", Proceedings of the IEEE World Congress on Computational Intelligence, Anchorage, May.
  23. Shi, Y. and Eberhart, R.C. (1999), "Empirical study of particle swarm optimization", Proceedings of the 1999 Congress on Evolutionary Computation, Washington, July, 3.
  24. Spielman, D.A. and Teng, S.H. (2004), "Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time", ACM (JACM), 51(3), 385-463. https://doi.org/10.1145/990308.990310
  25. Tamm, S. (1980), "Bird orientation: single homing pigeons compared with small flocks", Behav. Ecol. Sociobiol., 7(4), 319-322. https://doi.org/10.1007/BF00300672
  26. Tan, S.S. (2011), "A new swarm intelligent optimization algorithm: cell membrane optimization and its applications", Master. Dissertation, South China University of Technology, Guangzhou, China.
  27. Yan, X.H. (2010), "Research on path planning for mobile robot based on the biological intelligence", Ms.D. Dissertation, North China Electric University (Baoding), China.
  28. Yang, X.S. (2009), "Stochastic algorithms: foundations and applications", Springer, Berlin, Germany.
  29. Yi, T.H., Li, H.N. and Zhang, X.D. (2012a), "A modified monkey algorithm for optimal sensor placement in structural health monitoring", Smart. Mater. Struct., 21(10), 105033. https://doi.org/10.1088/0964-1726/21/10/105033
  30. Zhang, H.T., Chen, Z., Vicsek, T., Feng, G., Sun, L., Su, R. and Zhou, T. (2014), "Route-dependent switch between hierarchical and egalitarian strategies in pigeon flocks", Sci. Rep., 4, 1-7.
  31. Zhao, R.Q. and Tang, W.S. (2008), "Monkey algorithm for global numerical optimization", J. Uncertain Syst., 2(3), 165-176.
  32. Zuo, X., Chen, C., Tan, W. and Zhou, M. (2015), "Vehicle scheduling of an urban bus line via an improved multiobjective genetic algorithm", J. Intell. Transport. S., 16(2), 1030-1041.

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