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Evaluating the suitability of the firefly algorithm for optimization in optical and IoT networks

  • Rajni Rani ORCID logo EMAIL logo and Sharad Sharma ORCID logo
Published/Copyright: July 22, 2025
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Abstract

The Internet of things (IoT) is a quickly developing field that is defined by networked smart devices that communicate with one another to carry out intelligent tasks. Because of the energy, latency, and bandwidth limits, these networks must be optimized efficiently. This study investigates whether the firefly algorithm (FA), a metaheuristic inspired by nature, is appropriate for optimizing crucial parameters in IoT networks. The paper provides a thorough examination of the algorithm’s routing ability, energy economy, and convergence. The FA’s advantages and disadvantages in several IoT settings are illustrated through comparative analyses with alternative optimization methods like PSO and GA. According to the findings, FA offers a favorable trade-off between computational cost and performance, which makes it appropriate for lightweight and flexible IoT optimization tasks.


Corresponding author: Rajni Rani, Department of Electronics and Communication, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India, E-mail:

Acknowledgments

The authors express their sincere gratitude to Maharishi Markandeshwar (Deemed to be University), Mullana, for providing the necessary resources and support to conduct this research.

  1. Research ethics: This research does not involve human or animal participants, and hence ethical approval was not required.

  2. Informed consent: Not applicable. The study did not involve human participants requiring informed consent.

  3. Author contributions: R.R. conceptualized the research, conducted the simulations, and wrote the manuscript. S.S. supervised the research work and contributed to the review and editing of the manuscript. Both authors approved the final version.

  4. Use of large language models, AI and machine learning tools: No content generation or data analysis was performed by AI tools.

  5. Conflict of interest: The authors declare that they have no conflict of interest related to this publication.

  6. Research funding: This research received no specific grant from any funding agency in.

  7. Data availability: Data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Received: 2025-06-30
Accepted: 2025-07-10
Published Online: 2025-07-22

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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