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A scalable memory-enhanced swarm intelligence optimization method: fractional-order Bat-inspired algorithm

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Abstract

The Bat-inspired algorithm (BA), as one of the swarm intelligence algorithms, has a high potential for solving global optimization problems. This algorithm possesses an additional inherent capability compared to other swarm intelligence algorithms, which is the inclusion of a local search mechanism. Although this increases the convergence speed of the algorithm, excessive focus on exploitation, especially in the initial iterations, may lead to premature convergence and stagnation at the local optimum. In this paper, to address this drawback and strengthen the exploration capability while also achieving a balance between it and the exploitation capability, an improved version of BA, called fractional-order BA (FOBA), is introduced. The development of velocity and position vectors in FOBA, using the concept of fractional-order derivatives, extends the memory related to the previous behaviors of artificial bats and controls the convergence of the algorithm. To evaluate the proposed algorithm, ten well-known benchmark functions are used, and the results are compared with standard and state-of-the-art metaheuristic algorithms that have been introduced recently. Experimental results show that FOBA performs better than all compared algorithms. Furthermore, the proposed algorithm is used to optimize the weights and bias of the MLP neural network using six classification datasets. The results demonstrate the effectiveness and efficiency of FOBA.

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Data availability

The datasets used in this article are available in the UCI Machine Learning Repository, https://archive.ics.uci.edu.

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Authors and Affiliations

  1. Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

    Ahmad Esfandiari

  2. Department of Systems and Control, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Hamid Khaloozadeh

  3. Department of Computer and Mechatronics Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Faezeh Farivar

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  1. Ahmad Esfandiari
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Correspondence to Ahmad Esfandiari.

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Appendix A: Description of datasets

Appendix A: Description of datasets

More details of the features of the datasets and their description can be seen in Table 10.

Table 10 More details of datasets
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Esfandiari, A., Khaloozadeh, H. & Farivar, F. A scalable memory-enhanced swarm intelligence optimization method: fractional-order Bat-inspired algorithm. Int. J. Mach. Learn. & Cyber. 15, 2179–2197 (2024). https://doi.org/10.1007/s13042-023-02022-1

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