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Innovative Review on Artificial Bee Colony Algorithm and Its Variants

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Advances in Computing and Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The popular swarm-based algorithm is being inspired by the intelligent behavior of the honeybees that helps in finding the optimal solutions for getting the best food source. This paper is focused on highlighting the concept of the swan intelligence and the concept of the ABC algorithm, its variant, and also about its applications.

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References

  1. Neelima, S., Satyanarayana, N., & Krishna Murthy, P. (2016). A comprehensive survey on variants in artificial bee colony (ABC). International Journal of Computer Science and Information Technologies, 7(4).

    Google Scholar 

  2. Kumar, A., Kumar, D., & Jarial, S. K. (2017). A review on artificial bee colony algorithms and their applications to data clustering. Cybernetics and Information Technologies, 17.

    Google Scholar 

  3. Yurtkuran, A., & Emel, E. (2016). An enhanced artificial bee colony algorithm with solution acceptance rule and probabilistic multisearch. Hindawi Publishing Corporation Computational Intelligence and Neuroscience.

    Google Scholar 

  4. Qin, Q., Cheng, S., Zhang, Q., Li, L., & Shi, Y. (2015). Artificial bee colony algorithm with time-varying strategy. Hindawi Publishing Corporation Discrete Dynamics in Nature and Society.

    Google Scholar 

  5. Karaboga, D., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In Foundations of fuzzy logic and soft computing (pp. 789–798).

    Google Scholar 

  6. Baykasoglu, A., Ozbakir, L., & Tapkan, P. (2007). Artificial bee colony algorithm and its application to generalized assignment problem. In Swarm intelligence: Focus on ant and particle swarm optimization.

    Google Scholar 

  7. Quan, H., & Shi, X. (2008). On the analysis of performance of the improved artificial-bee-colony algorithm. Natural Computation.

    Google Scholar 

  8. Narasimhan, H. (2009). Parallel artificial bee colony (PABC) algorithm. In Nature & Biologically Inspired Computing.

    Google Scholar 

  9. Tsai, P. W., Pan, J. S., Liao, B. Y., & Chu, S. C. (2009). Enhanced artificial bee colony optimization. International Journal of Innovative Computing, Information and Control.

    Google Scholar 

  10. Akay, B., & Karaboga, D. (2010). A modified artificial bee colony algorithm for real parameter optimization. Information Sciences.

    Google Scholar 

  11. Banharnsakun, A., Achalakul, T., & Sirinaovakul, B. (2011). The best-so-far selection in artificial bee colony algorithm. Applied Soft Computing.

    Google Scholar 

  12. Shrimal, G., Rathi, R. (2014). A hybrid best so far artificial bee colony algorithm for function optimization. International Journal of Computer Science and Information Technologies.

    Google Scholar 

  13. Gao, W., Liu, S., & Huang, L. (2012). A global best artificial bee colony algorithm for global optimization. Journal of Computational and Applied Mathematics.

    Google Scholar 

  14. Bansal, J. C., Sharma, H., Arya, K. V., & Nagar, A. (2013). Memetic search in artificial bee colony algorithm. Soft Computing. https://doi.org/10.1007/s00500-013-1032-8.

  15. Kumar, S., Sharma, V. K., & Kumari, R. (2014). An improved memetic search in artificial bee colony algorithm. International Journal of Computer Science and Information Technologies.

    Google Scholar 

  16. Kumar, S., Sharma, V. K., & Kumari, R. (2014). Randomized memetic artificial bee colony algorithm. International Journal of Emerging Trends & Technology in Computer Science, 3(1), 52–62.

    Google Scholar 

  17. Kumar, S., Kumar, A., Sharma, V. K., & Sharma, H. (2014, August). A novel hybrid memetic search in artificial bee colony algorithm. In 2014 Seventh International Conference on Contemporary Computing (IC3) (pp. 68–73). IEEE.

    Google Scholar 

  18. Choubey, A., & Prajapati, G. L. (2015) An understanding of ABC algorithm and its applications. Indian Technical Research Organization.

    Google Scholar 

  19. Pandey, S., & Kumar, S. (2013). Enhanced artificial bee colony algorithm and it’s application to travelling salesman problem. HCTL Open International Journal of Technology Innovations and Research, 2.

    Google Scholar 

  20. Wang, Y., You, J., Hang, J., Li, C., & Cheng, L. (2018). An improved artificial bee colony (ABC) algorithm with advanced search ability. In International Conference on Electronics Information and Emergency Communication (ICEIEC).

    Google Scholar 

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Pooja, Shirmal, G. (2020). Innovative Review on Artificial Bee Colony Algorithm and Its Variants. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_14

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