A compact firefly algorithm for the variable selection problem in pharmaceutical ingredient determination

Bio-inspired metaheuristics have been used to solve various types of problems. Firefly algorithm (FA) is a nature-inspired metaheuristic based on the flash characteristics of fireflies. Variations of the FA implementation have been applied in the solution of optimization problems, including the problem of variable selection. Some works in the literature have applied several types of strategy in FA to select uncorrelated informative variables. On the other hand, other works have presented the use of compact evolutionary strategy such as compact genetic algorithm (CGA). However, as far as we know, there is still none work that uses such approach in the FA. Therefore, this paper proposes a compact firefly algorithm (CFA) implementation for the variable selection problem in multivariate calibration models to pharmaceutical ingredient determination. In an experiment comparison, our outcomes demonstrate that CFA is able to construct an accurate model with a more reduced number of variables selected. Additionally, CFA is up to 245% better than a standard GA implementation.