SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm
This is an incremental improvement for bio-inspired computing, addressing efficiency issues in population-based optimization algorithms.
The paper tackled the challenges of large population size and low diversity in Artificial Immune Systems by proposing a novel Symbiotic Artificial Immune System (SAIS) based on symbiotic relationships, achieving comparable performance to state-of-the-art methods and outperforming others on 26 benchmark problems.
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.