We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology. SAIS parallels the three key stages (i.e., mutualism, commensalism and parasitism) of population updating from the Symbiotic Organisms Search (SOS) algorithm. This parallel approach effectively addresses the challenges of large population size and enhances population diversity in AIS, which traditional AIS and SOS struggle to resolve efficiently. We conducted a series of experiments, which demonstrated that our SAIS achieved comparable performance to the state-of-the-art approach SOS and outperformed other popular AIS approaches and evolutionary algorithms across 26 benchmark problems. Furthermore, we investigated the problem of parameter selection and found that SAIS performs better in handling larger population sizes while requiring fewer generations. Finally, we believe SAIS, as a novel bio-inspired and immune-inspired algorithm, paves the way for innovation in bio-inspired computing with the symbiotic paradigm.

翻译：我们提出了一种新型人工免疫系统（AIS）：共生人工免疫系统（SAIS），其灵感来源于生物学中的共生关系。SAIS并行实现了共生生物搜索（SOS）算法中种群更新的三个关键阶段（即互利共生、共栖共生和寄生共生）。这种并行方法有效解决了AIS中大规模种群规模的问题，并增强了种群多样性，而传统AIS和SOS难以高效解决这些问题。我们进行了一系列实验，结果表明，在26个基准问题上，我们的SAIS达到了与最先进方法SOS相当的性能，并优于其他流行的AIS方法和进化算法。此外，我们研究了参数选择问题，发现SAIS在处理更大种群规模时表现更优，同时所需世代数更少。最后，我们相信SAIS作为一种新型仿生和免疫启发算法，为基于共生范式的仿生计算创新铺平了道路。