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 it to binary optimization problems. In this study, we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore, 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 well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC.

人工蜂群（ABC）算法是一种基于群体智能并受蜜蜂觅食行为启发的进化优化算法。由于 ABC 算法已被开发为通过在连续搜索空间中搜索来获得最优解，因此需要对其进行修改以将其应用于二元优化问题。在这项研究中，我们修改了 ABC 算法来解决二进制优化问题，并将其命名为改进的二进制 ABC（IbinABC）。所提出的方法由基于适应值和不同决策变量选择的更新机制组成。因此，我们的目标是通过增加其探索能力来防止 ABC 算法陷入局部最小值。我们将 IbinABC 算法与 ABC 的三种变体和文献中的其他元启发式算法进行了比较。为了进行比较，我们使用众所周知的 OR-Library 数据集，其中包含为无能力设施位置问题准备的 15 个问题实例。计算结果表明，该算法在收敛速度和鲁棒性方面均优于其他算法。该算法的源代码可在 https://github.com/rafetdurgut/ibinABC 获得。