  The Artificial Bee Colony (ABC) algorithm is an evolutionary optimization
algorithm based on swarm intelligence and inspired by the honey bees' food
search behavior. Since the ABC algorithm has been developed to achieve optimal
solutions by searching in the continuous search space, modification is required
to apply this method to binary optimization problems. In this paper, we improve
the ABC algorithm to solve binary optimization problems and call it the
improved binary Artificial Bee Colony (ibinABC). The proposed method consists
of an update mechanism based on fitness values and processing different number
of decision variables. Thus, we aim to prevent the ABC algorithm from getting
stuck in a local minimum by increasing its exploration ability. We compare the
ibinABC algorithm with three variants of the ABC and other meta-heuristic
algorithms in the literature. For comparison, we use the wellknown OR-Library
dataset containing 15 problem instances prepared for the uncapacitated facility
location problem. Computational results show that the proposed method is
superior to other methods in terms of convergence speed and robustness. The
source code of the algorithm will be available on GitHub after reviewing
process
