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Multi-objective Bird Swarm Algorithm

  • Dongmei WuEmail author
  • Hao Gao
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Most real-world optimization problems involve multiple objectives and parameters. In this paper, bird swarm algorithm (BSA) is modified with non-dominated sorting approach and parallel coordinates. A developed algorithm, known as multi-objective BSA (MOBSA) is proposed. When the external archive for non-dominated solutions is full to overflowing, the solution with greatest density would be rejected. The approaches were tested and compared on benchmark problems. Based on these results, the MOBSA has access to better convergence and spread of Pareto front.

Keywords

Bird swarm algorithm MOBSA Non-dominated sorting 

References

  1. 1.
    Serikawa, S., Lu, H.: Underwater image dehazing using joint trilateral filter. Comput. Electr. Eng. 40(1), 41–50 (2014)Google Scholar
  2. 2.
    Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., Serikawa, S.: Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J. (2017).  https://doi.org/10.1109/jiot.2017.2737479
  3. 3.
    Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 1–8 (2017)Google Scholar
  4. 4.
    Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., He, L., Li, X., Li, J., Serikawa, S.: Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput.: Pract. Exp. (2017).  https://doi.org/10.1002/cpe.3927
  5. 5.
    Xu, X., He, L., Lu, H., Gao, L., Ji, Y.: Deep adversarial metric learning for cross-modal retrieval. World Wide Web J. (2018).  https://doi.org/10.1007/s11280-018-0541-x
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Coello Coello, C.A., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of Congress Evolutionary Computation (CEC’2002), Honolulu, HI, vol. 1, pp. 1051–1056 (2002)Google Scholar
  8. 8.
    Knowles, J., Corne, D.: The Pareto archived evolution strategy: a new baseline algorithm for Pareto multi-objective optimisation. In: Proceedings of Congress on Evolutionary Computation (1999)Google Scholar
  9. 9.
    Meng, X.-B., et al.: A new bio-inspired optimisation algorithm: Bird Swarm Algorithm. J. Exp. Theoret. Artif. Intell. (2015)Google Scholar
  10. 10.
    Van Veldhuizen, D.A.V., Lamont, G.B.: Evolutionary Computation and Convergence to a Pareto Front, pp. 221–228. Stanford University California (1998)Google Scholar
  11. 11.
    Zhou, A., Jin, Y., Zhang, Q., et al.: Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 892–899 (2006)Google Scholar
  12. 12.
    Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Grefensttete, J.J. (ed.) Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Hillsdale, NJ (1987)Google Scholar
  13. 13.
    Fonseca, C.M., Fleming, P.J.: Multi-objective genetic algorithms made easy: selection sharing and mating restriction. In: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 45–52. Galesia. IET (1995)Google Scholar
  14. 14.
    Kalyanmoy, D.: Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol. Comput. 7(3), 205–230 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Nanjing University of Posts and TelecommunicationsNanjingChina

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