Smart flight and dynamic tolerances in the artificial bee colony for constrained optimization

This paper presents an adaptation of a novel algorithm based on the foraging behavior of honey bees to solve constrained numerical optimization problems. The modifications focus on improving the way the feasible region is approached by using a new operator which allows the generation of search directions biased by the best solution so far. Furthermore, two dynamic tolerances applied in the constraint handling mechanism help the algorithm to the generation of feasible solutions. The approach is tested on a set of 24 benchmark problems and its behavior is compared against the original algorithm and with respect to some state-of-the-art algorithms.