An improved artificial bee colony with modified augmented Lagrangian for constrained optimization

Artificial bee colony (ABC) algorithm has been successfully applied to solve constrained optimization problems (COPs). However, it is noteworthy that when using ABC to deal with COPs, the commonly used constraint-handling technique is the Deb’s feasibility-based rules. To our limited knowledge, the present ABC and its variants with augmented Lagrangian (AL) multiplier method have not been found applications to the COPs. In this paper, a novel constrained optimization method, named IABC-MAL, which integrates the benefit of the improved ABC (IABC) algorithm capability for obtaining the global optimum with the modified AL (MAL) method to handle constraints. This paper presents the first effort to integrate ABC algorithm with the AL method. To verify the performance of the proposed IABC-MAL, 24 well-known benchmark test problems at CEC2006, 18 benchmark test problems at CEC2010, and 5 engineering design problems are employed. Experiment results demonstrate that the proposed IABC-MAL algorithm shows better performance in comparison with other state-of-the-art algorithms from the literature.