论文标题
改进的二进制人工蜜蜂菌落算法
Improved Binary Artificial Bee Colony Algorithm
论文作者
论文摘要
人造蜜蜂菌落（ABC）算法是一种基于蜂群智能的进化优化算法，受蜜蜂的食物搜索行为的启发。由于已经开发了ABC算法来通过在连续搜索空间中进行搜索来实现最佳解决方案，因此需要修改以将此方法应用于二进制优化问题。在本文中，我们改进了ABC算法来解决二进制优化问题，并将其称为改进的二元人造蜜蜂菌落（Ibinabc）。所提出的方法由基于健身价值的更新机制和处理不同数量的决策变量组成。因此，我们旨在防止ABC算法通过提高其勘探能力而陷入本地最低限度。我们将IBINABC算法与文献中ABC的三种变体和其他元海拔算法进行了比较。为了进行比较，我们使用了众所周知的单元数据集，该数据集包含15个针对无能力的设施位置问题准备的问题实例。计算结果表明，就收敛速度和鲁棒性而言，所提出的方法优于其他方法。该算法的源代码在查看过程后将在GitHub上提供
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
