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A Novel Hybrid Algorithm Based on Bacterial Foraging Optimization and Grey Wolf Optimizer

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

A novel hybrid algorithm named GMBFO, with the combination between Grey Wolf Optimizer and the modified Bacterial Foraging Optimization, is presented in the paper. To improve the fixed chemotaxis step size in the standard BFO algorithm, the paper incorporates a nonlinear-decreasing adaptive mechanism into BFO. Besides that, an effective swarm learning strategy with the other three current global best individuals is proposed. In the dispersal and elimination step, we adopt the roulette wheel selection and local mutation mechanism to improve the diversity of the whole bacterial population. To testify the optimization performance of the proposed GMBFO, six benchmark functions with 45 dimensions are selected. Compared with BFO and the other three BFO variants, the GMBFO algorithm has an excellent capability in function optimization.

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References

  1. Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, New York (1995)

    Google Scholar 

  2. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  3. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  4. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22, 52–67 (2002)

    Article  Google Scholar 

  5. Tan, L., Lin, F., Wang, H.: Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows. Neurocomputing 151, 1208–1215 (2015)

    Article  Google Scholar 

  6. Panda, R., Naik, M.K.: A novel adaptive crossover bacterial foraging optimization algorithm for linear discriminant analysis based face recognition. Appl. Soft Comput. 30, 722–736 (2015)

    Article  Google Scholar 

  7. Yang, C., Ji, J., Liu, J., et al.: Structural learning of bayesian networks by bacterial foraging optimization. Int. J. Approximate Reasoning 69, 147–167 (2016)

    Article  MathSciNet  Google Scholar 

  8. Niu, B., Fan, Y., Wang, H., et al.: Novel bacterial foraging optimization with time-varying chemotaxis step. Int. J. Artif. Intell. 7, 257–273 (2011)

    Google Scholar 

  9. Niu, B., Wang, H., Tan, L., et al.: Improved BFO with adaptive chemotaxis step for global optimization. In: 2011 Seventh International Conference on Computational Intelligence and Security, pp. 76–80. IEEE Press, New York (2011)

    Google Scholar 

  10. Chen, Y., Li, Y., Wang. G., et al.: A novel bacterial foraging optimization algorithm for feature selection. Expert Syst. with Appl. 83, 1–17 (2017)

    Google Scholar 

  11. Chen, H., Zhang, Q., Luo. J., et al.: An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Appl. Soft Comput. 86, 1–24 (2020)

    Google Scholar 

  12. Wang, D., Qian, X., Ban. X., et al.: Enhanced bacterial foraging optimization based on progressive exploitation toward local optimum and adaptive raid. IEEE Access 7, 95725–95738 (2019)

    Google Scholar 

  13. Niu, B., Liu, J., Wu. T., et al.: Coevolutionary structure-redesigned-based bacterial foraging optimization. IEEE-ACM Trans. on Comput. Biol. Bioinform. 15, 1865–1876 (2018)

    Google Scholar 

  14. Biswas, A., Dasgupta, S., Das, S., et al.: Synergy of PSO and bacterial foraging optimization – a comparative study on numerical benchmarks. In: Corchado, E., Corchado, J.M., Abraham, A. (eds.) Innovations in Hybrid Intelligent Systems. ASC, vol. 44, pp. 255–263. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74972-1_34

  15. Pang, B., Song, Y., Zhang. C., et al.: Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy. Appl. Intell. 49, 1283–1305 (2019). https://doi.org/10.1007/s10489-018-1317-9

  16. Kim, D.H., Abraham, A., Cho. J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf. Sci. 177, 3918–3937 (2007)

    Google Scholar 

  17. Sarasiri, N., Suthamno, K., Sujitjorn, S.: Bacterial foraging-tabu search metaheuristics for identification of nonlinear friction model. J. Appl. Math. 2012, 1–24 (2012)

    Article  MathSciNet  Google Scholar 

  18. Turanoglu, B., Akkaya, G.: A new hybrid heuristic algorithm based on bacterial foraging optimization for the dynamic facility layout problem. Expert Syst. Appl. 98, 93–104 (2018)

    Article  Google Scholar 

  19. Yildiz, Y.E., Altun, O.: Hybrid achievement oriented computational chemotaxis in bacterial foraging optimization: a comparative study on numerical benchmark. Soft. Comput. 19, 3647–3663 (2015)

    Article  Google Scholar 

  20. Niu, B.: Bacterial Colony Optimization and Bionic Management. Science Press, China (2014). (in Chinese)

    Google Scholar 

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Xiaobing Gan & Baoyu Xiao

Authors
  1. Xiaobing Gan
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  2. Baoyu Xiao
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Corresponding author

Correspondence to Baoyu Xiao .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Gan, X., Xiao, B. (2020). A Novel Hybrid Algorithm Based on Bacterial Foraging Optimization and Grey Wolf Optimizer. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_40

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60801-9

  • Online ISBN: 978-3-030-60802-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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