An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

The firefly algorithm (FA) has become one of the most prominent swarm intelligence methods due to its efficiency in solving a wide range of various real-world problems. In this paper, an upgraded firefly algorithm (UFA) is proposed to further improve its performance in solving constrained engineering optimization problems. The main modifications of the basic algorithm are the incorporation of the logistic map and reduction scheme mechanism in order to perform fine adjustments of its control parameters, and employing a mutation operator in order to provide useful diversity in the population. Also, the proposed approach uses certain feasibility-based rules in order to guide the search to the feasible region of the search space, the improved scheme to handle the boundary constraints and the method for handling equality constraints. The UFA is tested on a set of 24 benchmark functions presented in CEC'2006 and nine widely used constrained engineering optimization problems. Comprehensive experimental results show that the overall performance of the UFA is superior to the FA and its recently proposed variants. Moreover, it achieves highly competitive results compared with other state-of-the-art metaheuristic techniques.