Um algoritmo inspirado em colônias de abelhas para otimização numérica com restrições

Abstract

Optimization problems are present in several areas of society and the use of bio-inspired algorithms to solve complex problems of this type has been growing constantly. The Artificial Bee Colony Algorithm (ABC) is a bio-inspired algorithm proposed in 2005 for solving multimodal and multidimensional optimization problems. The natural phenomenon that inspired the development of the ABC was intelligent behavior observed in bee colonies, more specifically in foraging. The ABC was initially proposed to be applied to solve unconstrained problems. This study evaluates the performance of ABC when applied in solving constrained optimization problems. For the treatment of constraints, penalty methods will be incorporated into the ABC. Several penalty methods, of different types, are analyzed with the goal of identifying which of these penalty methods offers better performance. Furthermore, possible limitations and care that should be taken when combining penalty methods to ABC are evaluated. The proposed algorithm is evaluated by solving optimization problems found in the literature. Several computational experiments are performed and graphs and tables are generated for demonstration of the obtained results which are also discussed.