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Firefly algorithm: recent advances and applications
Open accessAbstract
Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higherdimensional optimisation problems.
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Cited by
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Kar, A. K. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.018 · Cited by 578
Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.134 · Cited by 312
Ibrahim, I. A., & Khatib, T. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.02.006 · Cited by 267
Yuvaraj, T., Devabalaji, K. R., Kumar, J. A., Thanikanti, S. B., & Nwulu, N. I. (2024) · IEEE Access · doi:10.1109/access.2023.3349274 · Cited by 249
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Kansal, N. J., & Chana, I. (2016) · Journal of Grid Computing · doi:10.1007/s10723-016-9364-0 · Cited by 151
Metadata
- Authors
- Yang, X. S., & He, X.
- Venue
- International Journal of Swarm Intelligence
- Publisher
- Inderscience Publishers
- Published
- 2013
- Volume
- 1
- Issue
- 1
- Pages
- 36
- ISSN
- 2049-4041 (ISSN-L)
- 2049-405X
- OpenAlex
- W2070145903
- Cited by
- 858
- References
- 0
- FWCI
- 47.12
- Type
- journal article
- Language
- en
- Open access
- green
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