Swarm Intelligence algorithms have gained significant attention in recent years as a means of solving complex and non-deterministic problems. These algorithms are inspired by the collective behavior of natural creatures, and they simulate this behavior to develop intelligent agents for computational tasks. One such algorithm is Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ants and their pheromone laying mechanism. ACO is used for solving difficult problems that are discrete and combinatorial in nature. Part-of-Speech (POS) tagging is a fundamental task in natural language processing that aims to assign a part-of-speech role to each word in a sentence. In this research paper, proposed a high-performance POS-tagging method based on ACO called ACO-tagger. This method achieved a high accuracy rate of 96.867%, outperforming several state-of-the-art methods. The proposed method is fast and efficient, making it a viable option for practical applications.

翻译：群集智能算法在解决复杂和非确定性问题方面越来越受到关注。这些算法受到自然生物集体行为的启发，并模拟这种行为开发计算任务的智能代理。其中一种算法是蚁群优化（ACO），受到蚂蚁觅食和它们的信息素分泌机制的启发。ACO用于解决离散和组合的困难问题。词性标注是自然语言处理中的一个基本任务，其目的是为句子中的每个单词分配一个词性角色。在这篇研究论文中，我们提出了一种基于ACO的高性能POS标注方法，称为ACO-tagger。该方法的准确率达到了96.867％，优于几种最先进的方法。所提出的方法快速高效，是实际应用的可行选项。