Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

群体智能 (SI) 算法经常用于解决复杂的优化问题。当在合理的响应时间内为 NP 难题寻求好的解决方案时，特别使用 SI。当此类问题具有非常高的维度、动态性质或存在内在复杂的相互交织的自变量时，SI 算法的计算成本可能仍然太高。因此，需要新的方法和硬件支持来加快处理速度。如今，随着 GPU 和多核处理的普及，并行版本的 SI 算法可以在这些问题上提供所需的性能。本文旨在描述此类方法的最新技术，总结所解决的关键点，并确定可以更好地解决的研究差距。这篇评论的范围考虑了最近的论文，主要集中在最常用的 SI 算法的并行实现上。嵌套并行的使用特别令人感兴趣，因为一级并行通常不足以利用当代并行硬件的计算能力。来源是主要的科学数据库，并根据本文献综述的设定要求进行过滤。