A comprehensive review of firefly algorithms

By Iztok Fister, Xin-She Yang, Janez Brest. Swarm and Evolutionary Computation, 2013

1,280 citations recorded by the source corpus.

Abstract The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/85ff66f629833a559149ecc2eb6f14c86c784171

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Biomedical Engineering

Citations in the corpus

Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, background): “There are many nature-inspired algorithms in the current literature, it is estimated there are more than 100 different algorithms and variants [13, 9, 14, 15, 16, 17, 18].”
Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions (2016, background): “Many other evolutionary algorithms such as Artificial Bee Colony (ABC) [22], Bacterial Foraging Optimization (BFO) [23], Bio Inspired Neural Networks [24, 25], and Fire Fly algorithm [26] are often trapped in local optimum, and bear high computational cost.”
A Brief Review of Nature-Inspired Algorithms for Optimization (2013, methodology): “A na rc hi c so ci et y op tim iz at io n Sh ay eg hi an d D ad as hp ou r [6 6] H ar m on y se ar ch G ee m et al .”
A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “The flashing characteristics are formulated and used as functions to optimize combinatorial problems [154].”
A survey on new generation metaheuristic algorithms (2019, background): “The distributional properties of randomization is an important facility for controlling the balance between exploration and exploitation, and attracted a lot of attention in the literature (Fister et al., 2013; Gandomi et al., 2013c) The parameter γ decides the diversity of the attractiveness.”