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SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm

Junhao Song, Yingfang Yuan, Wei Pang

TL;DR

SAIS addresses the challenge of maintaining population diversity and computational efficiency in Artificial Immune Systems by adopting a symbiotic paradigm inspired by biology. It parallelizes the three SOS phases—mutualism, commensalism, and parasitism—across three subpopulations, augmented by memory cells, to update antibodies concurrently and preserve diversity. Across 26 unconstrained benchmark problems, SAIS achieves performance comparable to SOS and often outperforms other AIS and evolutionary approaches, with larger population sizes further improving success rates. An ablation study confirms that leveraging all three symbiotic operators yields the best convergence, underscoring the value of the symbiotic paradigm for bio-inspired computing.

Abstract

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.

SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm

TL;DR

SAIS addresses the challenge of maintaining population diversity and computational efficiency in Artificial Immune Systems by adopting a symbiotic paradigm inspired by biology. It parallelizes the three SOS phases—mutualism, commensalism, and parasitism—across three subpopulations, augmented by memory cells, to update antibodies concurrently and preserve diversity. Across 26 unconstrained benchmark problems, SAIS achieves performance comparable to SOS and often outperforms other AIS and evolutionary approaches, with larger population sizes further improving success rates. An ablation study confirms that leveraging all three symbiotic operators yields the best convergence, underscoring the value of the symbiotic paradigm for bio-inspired computing.

Abstract

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.
Paper Structure (14 sections, 4 equations, 7 figures, 4 tables, 2 algorithms)

This paper contains 14 sections, 4 equations, 7 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Symbiotic Relationships in Biology
  4. Research on the AIS and Symbiotic AIS Applications
  5. Symbiotic Artificial Immune Systems
  6. Structure and Design of SAIS
  7. Mutualism, Commensalism and Parasitism
  8. Pseudocode for SAIS
  9. Experiments
  10. The Analysis of Max Iteration and Population Size
  11. SAIS on 26 Benchmark Unconstrained Mathematical Problems
  12. Comparison of SAIS with other AIS
  13. Ablation Study
  14. Conclusions

Figures (7)

  • Figure 1: Examples of Mutualism, Commensalism and Parasitism in Biology (CC BY-ND 2.0 Licenses)
  • Figure 2: SAIS Algorithm Flow Chart
  • Figure 3: Comparison of Population Updating Methods in SAIS and SOS
  • Figure 4: The change in the average fitness values of four models over 30 experimental runs on Benchmark Easom
  • Figure 5: The change in the average fitness values of four models over 30 experimental runs on Benchmark Michalewicz2
  • ...and 2 more figures