Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values. This updating mechanism can be improved to utilize the survival-of-the-fittest principle by adopting different functions such as linear, quadratic, and sinusoidal. In this paper, a novel BDA is proposed. The algorithm uses different strategies to update the values of its five main coefficients to tackle Feature Selection (FS) problems. Three versions of BDA have been proposed and compared against the original DA. The proposed algorithms are Linear-BDA, Quadratic-BDA, and Sinusoidal-BDA. The algorithms are evaluated using 18 well-known datasets. Thereafter, they are compared in terms of classification accuracy, the number of selected features, and fitness value. The results show that Sinusoidal-BDA outperforms other proposed methods in almost all datasets. Furthermore, Sinusoidal-BDA exceeds three swarm-based methods in all the datasets in terms of classification accuracy and it excels in most datasets when compared in terms of the fitness function value. In a nutshell, the proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling FS problems.

蜻蜓算法（DA）是一种最近的基于群体的优化方法，它模仿理想化蜻蜓的狩猎和迁移机制。最近，已经提出了二进制DA（BDA）。在算法迭代过程中，BDA使用随机值更新其五个主要系数。通过采用诸如线性，二次和正弦曲线之类的不同功能，可以改进此更新机制以利用适者生存的原理。本文提出了一种新颖的BDA。该算法使用不同的策略来更新其五个主要系数的值，以解决特征选择（FS）问题。已经提出了三种版本的BDA，并将其与原始DA进行了比较。提出的算法是线性BDA，二次BDA和正弦BDA。使用18个众所周知的数据集对算法进行评估。之后，根据分类准确性，所选特征的数量和适用性值对它们进行比较。结果表明，在几乎所有数据集中，正弦BDA均优于其他建议方法。此外，就分类精度而言，正弦BDA在所有数据集中均超过了三种基于群体的方法，并且在适合度函数值方面，它在大多数数据集中均表现出色。简而言之，提出的正弦BDA优于可比的特征选择算法，并且提出的更新机制在解决FS问题时对算法性能有很大影响。在分类准确性方面，正弦BDA在所有数据集中均超过了三种基于群体的方法，在适应度函数值方面，它在大多数数据集中均表现出色。简而言之，提出的正弦BDA优于可比的特征选择算法，并且提出的更新机制在解决FS问题时对算法性能有很大影响。在分类准确性方面，正弦BDA在所有数据集中均超过了三种基于群体的方法，在适应度函数值方面，它在大多数数据集中均表现出色。简而言之，提出的正弦BDA优于可比的特征选择算法，并且提出的更新机制在解决FS问题时对算法性能有很大影响。