# Firefly Algorithm: Recent Advances and Applications

arXiv: https://arxiv.org/abs/1308.3898 · HTML: https://mlsanity.com/paper/1308.3898/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2013.

## Built on by (most cited first, 5 of 5)

- [Adaptive Regret for Bandits Made Possible: Two Queries Suffice](https://mlsanity.com/paper/2401.09278/) (2024)
- [VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making](https://mlsanity.com/paper/2202.03429/) (2022)
- [A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems](https://mlsanity.com/paper/2302.08855/) (2023)
- [Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models](https://mlsanity.com/paper/2404.10162/) (2024)
- [Semantic-Anchored, Class Variance-Optimized Clustering for Robust Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2501.14401/) (2025)

## Abstract (arXiv)

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 higher-dimensional optimization problems.