Advertisement

Adaptive Multi-swarm Bat Algorithm (AMBA)

  • Reshu ChaudharyEmail author
  • Hema Banati
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

Modified shuffled multi-population bat algorithm (MSMPBat) is a recently proposed swarm algorithm. It divides its population into multiple sub-populations (SPs), each of which uses different parameter settings and evolves independently using an enhanced search mechanism. For information exchange among these SPs, a solution from one SP is copied to the next after every generation. This process leads to duplication of solutions over time. To overcome this drawback, different techniques are introduced. Opposition-based learning is used to generate a diverse starting population. For information exchange, if a solution comes too close to the swarm best, only then it is sent (moved, not copied) to another swarm. Four techniques are proposed to select this second swarm. Initially, the selection probability of each technique is same. The algorithm adaptively updates these probabilities based on their success rate. The swarm which gave up the solution uses a modified opposition-based learning technique to generate a new solution. These changes help to maintain the overall diversity of the population. The proposed approach, namely, adaptive multi-swarm bat algorithm (AMBA), is compared to six algorithms over 20 benchmark functions. Results establish the superiority of adaptive multi-swarm bat algorithm.

Keywords

Bat algorithm Multi-swarm optimization Adaptive algorithm Numerical optimization 

References

  1. 1.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, UK (2010)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. Australia (1995)Google Scholar
  3. 3.
    Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms, 2nd edn. John Wiley and Sons, USA (2004)zbMATHGoogle Scholar
  4. 4.
    Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) Stochastic Algorithms: Foundations and Appplications, SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer-Verlag, Berlin (2009)CrossRefGoogle Scholar
  5. 5.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010). In: Gonzalez, J.R. et al. (eds.) Studies in Computational Intelligence, vol. 284, pp. 65 –74, Springer, Berlin (2010)CrossRefGoogle Scholar
  6. 6.
    Alihodzic, A., Tuba, M.: Improved bat algorithm applied to multilevel image thresholding. Sci. World J. 2014, 16 (2014), Article ID 176718Google Scholar
  7. 7.
    Xiao, L., Qian, F., Shao, W.: Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm. Energy Convers. Manag. 143, 410–430 (2017)CrossRefGoogle Scholar
  8. 8.
    Naderi, M., Khamehchi, E.: Well placement optimization using metaheuristic bat algorithm. J. Petrol. Sci. Eng. 150, 348–354 (2017)CrossRefGoogle Scholar
  9. 9.
    Rahmani, M., Ghanbari, A., Ettefagh, M.M.: Robust adaptive control of a bio-inspired robot manipulator using bat algorithm. Expert Syst. Appl. 56, 164–176 (2016)CrossRefGoogle Scholar
  10. 10.
    Banati, H., Chaudhary, R.: Multi-Modal bat algorithm with improved search (MMBAIS). J. Comput. Sci. 23, 130–144 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Chaudhary, R., Banati, H.: Shuffled multi-population bat algorithm (SMPBat). In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 541–547. IEEE, Udupi (2017)Google Scholar
  12. 12.
    Chaudhary, R., Banati, H.: Modified shuffled multi-population bat algorithm. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 943–951. IEEE, Bangalore (2018)Google Scholar
  13. 13.
    Al-Betar, M.A., Awadallah, M.A.: Island bat algorithm for optimization. Expert Syst. Appl. (2018).  https://doi.org/10.1016/j.eswa.2018.04.024CrossRefGoogle Scholar
  14. 14.
    Al-Betar, M.A., Awadallah, M.A., Faris, H., Yang, X.S., Khader, A.T., Alomari, O.A.: Bat-inspired algorithms with natural selection mechanisms for global optimization. Neurocomputing 273, 448–465 (2018)CrossRefGoogle Scholar
  15. 15.
    Meng, X.-B., Gao, X.Z., Liu, Y., Zhang, H.: A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization. Expert Syst. Appl. 42, 6350–6364 (2015)CrossRefGoogle Scholar
  16. 16.
    Topal, A.O., Altun, O.: A meta-heuristic algorithm: dynamic virtual bats algorithm. Inf. Sci. 354, 222–235 (2016)CrossRefGoogle Scholar
  17. 17.
    Banati, H., Chaudhary, R.: Enhanced shuffled bat algorithm (EShBAT). In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 731–738. IEEE, Jaipur (2016)Google Scholar
  18. 18.
    Chakri, A., Khelif, R., Benouaret, M., Yang, X.S.: New directional bat algorithm for continuous optimization problems. Expert Syst. Appl. 69, 159–175 (2017)CrossRefGoogle Scholar
  19. 19.
    Ahandani, M.A., Alavi-Rad, H.: Opposition-based learning in the shuffled differential evolution algorithm. Soft. Comput. 16, 1303–1337 (2012)CrossRefGoogle Scholar
  20. 20.
    Derrac, J., Garcia, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1, 3–18 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of DelhiDelhiIndia
  2. 2.Dyal Singh CollegeDelhiIndia

Personalised recommendations