Firefly algorithm: overview, applications, and modifications

The Firefly Algorithm (FA) is a nature-inspired, population-based metaheuristic developed by Xin-She Yang in 2007 that mimics the flashing behavior of fireflies. This paper presents an in-depth investigation of the Firefly Algorithm, beginning with its biological inspiration and mathematical formulation, and proceeding to a comprehensive discussion of its diverse applications across engineering, image segmentation, scheduling, and other domains. In addition, various modifications of the original FA including Gaussian variants, chaotic variants, and opposition- and dimensional-based improvements are reviewed and compared. Two detailed tables summarize the primary applications and modifications, respectively, while discussions highlight the trade-offs between exploration and exploitation inherent in the algorithm. The paper concludes with an analysis of current achievements and future research directions in firefly-based optimization techniques.

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Publication Details

Journal
Journal of Image Processing and Intelligent Remote Sensing
Published
2025-11-06
DOI
https://doi.org/10.55529/jipirs.52.1.16
Primary Topic
Metaheuristic Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Firefly algorithm: overview, applications, and modifications

Renas Rajab Asaad, Saman M. Almufti‎, Awaz Ahmed Shaban, Rasan Ismael Ali
Journal of Image Processing and Intelligent Remote Sensing
Metaheuristic Optimization Algorithms Research
article

Firefly algorithm: overview, applications, and modifications

Renas Rajab Asaad, Saman M. Almufti‎, Awaz Ahmed Shaban, Rasan Ismael Ali
article en

Abstract

The Firefly Algorithm (FA) is a nature-inspired, population-based metaheuristic developed by Xin-She Yang in 2007 that mimics the flashing behavior of fireflies. This paper presents an in-depth investigation of the Firefly Algorithm, beginning with its biological inspiration and mathematical formulation, and proceeding to a comprehensive discussion of its diverse applications across engineering, image segmentation, scheduling, and other domains. In addition, various modifications of the original FA including Gaussian variants, chaotic variants, and opposition- and dimensional-based improvements are reviewed and compared. Two detailed tables summarize the primary applications and modifications, respectively, while discussions highlight the trade-offs between exploration and exploitation inherent in the algorithm. The paper concludes with an analysis of current achievements and future research directions in firefly-based optimization techniques.

Journal of Image Processing and Intelligent Remote Sensing(52)
Public Knowledge (US)
Openalex Percentile: Top 83%
Metaheuristic Optimization Algorithms Research
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