A multiobjective firefly approach using beta probability distribution for electromagnetic optimization problems

Current research on optimization methods is increasingly focused on biology-inspired metaheuristics as efficient tools for the solution of many electromagnetic optimization problems. The firefly algorithm (FA) is an algorithm of this class, and is based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be represented in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate a biology-inspired algorithm. This paper briefly introduces the basics of FA and its multiobjective version (MOFA) and proposes a novel multiobjective variant which uses the beta probability distribution (MOBFA) in the tuning of control parameters, which is useful to maintain the diversity of solutions, as well as the use of crowding-based archiving of the Pareto solutions. Numerical results refer to a simple analytical benchmark as well as a multiobjective constrained brushless dc motor design problem, both showing that the resulting MOBFA algorithm outperforms the standard one.