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：群体智能算法近年来作为解决复杂非确定性问题的有效手段而备受关注。这类算法受自然界生物集体行为的启发，通过模拟该行为为计算任务开发智能体。其中蚁群优化（ACO）算法模仿蚂蚁觅食行为及其信息素释放机制，用于解决离散与组合特性的难解问题。词性标注（POS tagging）是自然语言处理中的基础任务，旨在为句子中的每个单词赋予词性角色。本文提出了一种基于ACO的高性能词性标注方法——ACO-tagger。该方法实现了96.867%的高准确率，超越了多种现有最优方法，且兼具快速高效的特点，为实际应用提供了可行方案。