Firefly Algorithm (FA) is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies. The basic idea behind FA is to mimic the flashing behavior of fireflies, where the fireflies use their light to attract other fireflies for mating purposes.
The brightness of a firefly-s light depends on its attractiveness, which is determined by its distance from other fireflies and brightness.
FA starts with an initial population of fireflies randomly distributed in the search space. The attractiveness of each firefly is determined by its objective function value, and the attractiveness of other fireflies guides the movement of each firefly. The movement of a firefly towards another firefly is based on the distance between the two fireflies and the brightness of the other firefly-s light.
The attraction coefficient guides the movement, which determines the strength of attraction between the two fireflies. FA can be applied to a wide range of optimization problems, including function optimization, parameter optimization, and feature selection.
One of the advantages of FA is its ability to handle complex and multimodal optimization problems. However, FA also has some limitations, such as its sensitivity to the parameters used in the algorithm and its tendency to get stuck in local optima.
To address these limitations, various modifications and enhancements to the basic FA algorithm, such as adaptive parameter tuning, hybridization with other optimization algorithms, and dynamic neighborhood structures.