Abstract
One popular example of metaheuristic algorithms from the swarm intelligence family is the Bat algorithm (BA). The algorithm was first presented in 2010 by Yang and quickly demonstrated its efficiency in comparison with other common algorithms. The BA is based on echolocation in bats. The BA uses automatic zooming to strike a balance between exploration and exploitation by imitating the deviations of the bat’s pulse emission rate and loudness as it searches for prey. The BA maintains solution diversity using the frequency-tuning technique. In this way, the BA can quickly and efficiently switch from exploration to exploitation. Therefore, it becomes an efficient optimizer for any application when a quick solution is needed. In this paper, an improvement on the original BA has been made to speed up convergence and make the method more practical for large applications. To conduct a comprehensive comparative analysis between the original BA, the modified BA proposed in this paper, and other state-of-the-art bio-inspired metaheuristics, the performance of both approaches is evaluated on a standard set of 23 (unimodal, multimodal, and fixed-dimension multimodal) benchmark functions. Afterwards, the modified BA was applied to solve a real-world job scheduling problem in hotels and restaurants. Based on the achieved performance metrics, the proposed MBA establishes better global search ability and convergence than the original BA and other approaches
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References
Fister Jr, I., Fister, D., Yang, X.-S.: A hybrid bat algorithm (2013). arXiv:1303.6310
Ab Wahab, M.N., Nefti-Meziani, S., Atyabi, A.: A comprehensive review of swarm optimization algorithms. PloS One 10(5), e0122827 (2015)
A. S. Shamsaldin, T. A. Rashid, R. A. Al-Rashid Agha, N. K. Al-Salihi, and M. Mohammadi, “Donkey and smuggler optimization algorithm: A collaborative working approach to path finding,” Journal of Computational Design and Engineering, vol. 6, no. 4, pp. 562–583 (2019)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Ramesh, B., Mohan, V.C.J., Reddy, V.V.: Application of bat algorithm for combined economic load and emission dispatch. Int. J. Electr. Eng. Telecommun. 2(1), 1–9 (2013)
Yılmaz, S., Kucuksille, E.U., Cengiz, Y.: Modified bat algorithm. Elektronika ir Elektrotechnika 20(2), 71–78 (2014)
Fister, I., Rauter, S., Yang, X.-S., Ljubič, K., Fister, I., Jr.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149, 993–1002 (2015)
K. Kiełkowicz and D. Grela, “Modified bat algorithm for nonlinear optimization,” International Journal of Computer Science and Network Security (IJCSNS), pp. 46–50 (2016)
Cai, X., Gao, X.-Z., Xue, Y.: Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int. J. Bio-Inspired Comput. 8(4), 205–214 (2016)
Osaba, E., Yang, X.-S., Fister, I., Jr., Del Ser, J., Lopez-Garcia, P., Vazquez-Pardavila, A.J.: A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm Evolut. Comput. 44, 273–286 (2019)
X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74, Springer (2010)
Abdullah, J.M., Ahmed, T.: Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7, 43473–43486 (2019)
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Rashid, T.A. et al. (2021). An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants. In: Pap, E. (eds) Artificial Intelligence: Theory and Applications. Studies in Computational Intelligence, vol 973. Springer, Cham. https://doi.org/10.1007/978-3-030-72711-6_9
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DOI: https://doi.org/10.1007/978-3-030-72711-6_9
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