The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Learning(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, Swarm and Evolutionary (SWEVO) Algorithms have also shown significant performance to improve the efficiency of the IDS models. This survey covers SWEVO-based IDS approaches such as Genetic Algorithm(GA), Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Artificial Bee Colony Optimization(ABC), Firefly Algorithm(FA), Bat Algorithm(BA), and Flower Pollination Algorithm(FPA). The paper also discusses applications of the SWEVO in the field of IDS along with challenges and possible future directions.

数据的增长和攻击类别要求通过机器学习（ML）和深度学习（DL）技术有效地使用入侵检测系统（IDS）。除了ML和DL技术外，群体和进化（SWEVO）算法还显示出显着的性能，可提高IDS模型的效率。这项调查涵盖了基于SWEVO的IDS方法，例如遗传算法（GA），蚁群优化（ACO），粒子群优化（PSO），人工蜂群优化（ABC），萤火虫算法（FA），蝙蝠算法（BA），和花授粉算法（FPA）。本文还讨论了SWEVO在IDS领域中的应用以及挑战和可能的未来方向。