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Cluster Computing

, Volume 22, Supplement 2, pp 3271–3284 | Cite as

An optimized IS-APCPSO algorithm for large scale complex traffic network

  • Ke Huang
  • Hao Lan ZhangEmail author
  • Gelan Yang
Article
  • 86 Downloads

Abstract

Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.

Keywords

Optimization IS-APCPSO algorithm Traffic network Immune selection 

Notes

Acknowledgements

This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ17G030007. This research was supported by Ningbo Soft Science Project under Grant No. 2017A10070, Ningbo Innovation Team (Grant No. 2016C11024), Zhejiang Provincial Natural Science Foundation of China under Grant No. LY14G010004, and National Natural Science Foundation of China under Grant No. 61272480.

References

  1. 1.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)Google Scholar
  2. 2.
    Robinson, J., Sinton, S., Yahya, R.S.: Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: IEEE Antennas and Propagation Society International Symposium, San Antonio, pp. 314–317 (2002)Google Scholar
  3. 3.
    Zhang, Y.D., Wang, S.H., Ji, G.L.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. 2015, 1–38 (2015)Google Scholar
  4. 4.
    Chen, K.H., Wang, K.J., Tsai, M.L.: Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. BMC Bioinform. 15 (2014)Google Scholar
  5. 5.
    Mahapatra, P.K., Ganguli, S., Kumar, A.: A hybrid particle swarm optimization and artificial immune system algorithm for image enhancement. Soft. Comput. 19(8), 2101–2109 (2015)Google Scholar
  6. 6.
    Beheshti, Z., Shamsuddin, S.M.H., Beheshti, E.: Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft. Comput. 18(11), 2253–2270 (2014)Google Scholar
  7. 7.
    Darzi, S., Kiong, T.S., Islam, M.T.: Null steering of adaptive beamforming using linear constraint Minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm. Sci. World J. (2014)Google Scholar
  8. 8.
    Du, H., Liu, D.C., Zhang, M.H.: A hybrid algorithm based on particle swarm optimization and artificial immune for an assembly job shop scheduling problem. Math. Probl. Eng. 2016, 1–10 (2016)Google Scholar
  9. 9.
    Lin, G.H., Zhang, J., Liu, Z.H.: Immune comprehensive learning particle swarm optimization algorithm. Appl. Res. Comput. 31(11), 3229–3233 (2014)Google Scholar
  10. 10.
    Fan, L.L., Aijia, Q.Y.: Hybrid immune PSO algorithm for engineering optimization problems. In: 12th International Conference on Natural Computation, pp. 179–185 (2016)Google Scholar
  11. 11.
    Zhang, X., Fan, H., Li, H.Y.: An improved particle swarm optimization algorithm based on immune system. In: 7th International Conference on Swarm Intelligence, vol. 9712, pp. 331–340 (2016)Google Scholar
  12. 12.
    Lin, G.H., Zhao, K.Y., Wan, Q.: Takagi-sugeno fuzzy model identification using coevolution particle swarm optimization with multi-strategy. Appl. Intell. 45(1), 187–197 (2016)Google Scholar
  13. 13.
    Idris, I., Selamat, A.: Improved email spam detection model with negative selection algorithm and particle swarm optimization. Appl. Soft Comput. 22, 11–27 (2014)Google Scholar
  14. 14.
    Huang, K.: Optimization model and algorithm of urban traffic network considering environmental pollution control. Ph.D. dissertation, Dept. School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu, P. R. China (2011)Google Scholar
  15. 15.
    Huang, K., Zhang, H., Wang, Y., Yu, C.: An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection. In: Proceedings of the 2017 International Conference on Machine Learning and Cybernetics (ICMLC 2017), Ningbo, China, 9–12 July, pp. 105–110 (2017)Google Scholar
  16. 16.
    Lu, G., Tan, D., Zhao, H.: Improvement on regulating definition of antibody density of immune algorithm. In: Proceedings of the 9th international conference on neural information processing, Singapore, No. 5, pp. 2669–2672 (2002)Google Scholar
  17. 17.
    Lü, Z.S., Hou, Z.R.: Particle swarm optimization with adaptive mutation. Acta Electronica Sinica 32(3), 416–420 (2004)Google Scholar
  18. 18.
    Kou, X., Swarm intelligence algorithms and their applications. Ph.D. dissertation, Dept. College of Science, Xidian Univ., Xi’an, P. R. China (2009)Google Scholar
  19. 19.
    Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. Evolutionary Programming VII, Lecture notes in computer science 1447. Springer-Berlin, San Diego, pp. 591–600 (1998)Google Scholar
  20. 20.
    M. Lϕvbjerg, T. K. Rasmussen, and T. Krink, “Hybrid particle swarm optimiser with breeding and subpopulations”, proceedings of the Third Genetic and Evolutionary Computation Conference, San Francisco, USA, 2001Google Scholar
  21. 21.
    Zhou Shenpei, “Research on Traffic Signal Control Strategies in Urban Intersections Based on Emission Factors”, Ph.D. dissertation, Dept. College of Automation, Wuhan Univ. of Technology, Wuhan, P. R. China, 2009Google Scholar
  22. 22.
    de Castro, L.N., Timmis, J.: An artificial immune network for multimodal function optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Hawaii, USA, 2002. IEEE, USA, vol. 1, pp. 699–704 (2002)Google Scholar
  23. 23.
    Jiao, L.C., Du, H.F.: Immune optimization computation, learning and recognition, pp. 1–464. Science Press, Beijing (2006)Google Scholar
  24. 24.
    Xue, W.T., Wu, X.B., Shan, L.: Immune chaotic network algorithm for multimodal function optimization. J. Syst. Simul. 22(4), 915–920 (2010)Google Scholar
  25. 25.
    Koziel, S., Michalewicz, Z.: Evolutionary algorithm, homomorphous mappings, and constrained parameter optimization. Evolut. Comput. 7(1), 19–44 (1999)Google Scholar
  26. 26.
    Li, X.Y., Tian, P., Kong, M.: A new particle swarm optimization for solving constrained optimization problems. J. Syst. Manag. 16(2), 120–129 (2007)Google Scholar
  27. 27.
    Aiyoshi, E., Shimizu, K.: A solution method for the static constrained Stackelberg problem via penalty method. IEEE Trans. Autom. Control 29(12), 1111–1114 (1984)Google Scholar
  28. 28.
    Bard, J.: Convex two-level optimization. Math. Program. 40, 15–27 (1988)Google Scholar
  29. 29.
    Colson, B., Marcotte, P., Savard, G.: Bilevel programming: a survey. Q. J. Belg. Fr. Ital. Oper. Res. Soc. 3, 87–107 (2005)Google Scholar
  30. 30.
    Li, X.Y., Tian, P.: Particle swarm optimization for solving bilevel programming problems. J. Manag. Sci. China 11(5), 41–51 (2008)Google Scholar
  31. 31.
    Liu, B., Wang, L., Jin, Y.H., Tang, F., Huang, D.X.: Improve particle swarm optimization combined with chaos. Chaos, Solitons Fractals 25(5), 1261–1271 (2005)Google Scholar
  32. 32.
    Zhao, Z.G., Gu, X.Y., Li, T.S.: Particle swarm optimization for bi-level programming problem. Syst. Eng. Theory Pract. 27(8), 92–98 (2007)Google Scholar
  33. 33.
    Suwansirikul, C., Friesz, T.L., Tobin, R.L.: Equilibrium decomposed optimization: a heuristic for the continuous equilibrium network design problem. Transp. Sci. 21, 254–263 (1987)Google Scholar
  34. 34.
    Zhang, H.Z., Gao, Z.Y., Zhang, B.: Model and algorithm of transportation network design for emission reduction. China Civ. Eng. J. 39(11), 114–119 (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.SCDM CenterNingbo Institute of Technology Zhejiang UniversityNingboChina
  2. 2.School of Information Science and EngineeringHunan City UniversityHeshanChina

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