Swarm Performance Indicators: Metrics for Robustness, Fault Tolerance, Scalability and Adaptability

TL;DR

This paper introduces quantitative metrics called Swarm Performance Indicators to measure robustness, fault tolerance, scalability, and adaptability in swarm and multi-agent systems, addressing the lack of standardized evaluation tools.

Contribution

It defines and formalizes quantitative metrics for key swarm features, enabling objective assessment beyond task-specific performance.

Findings

Swarm Performance Indicators effectively quantify robustness and scalability.

Metrics can be applied to both swarm and centralized multi-agent systems.

The approach facilitates comparison and validation of swarm capabilities.

Abstract

Swarms have distributed control and so are assumed to inherently have superior robustness, scalability and adaptability compared to centralised multi-agent systems. However, these features have generally only been defined qualitatively and there is a lack of quantitative metrics and experimental measures for the claimed parameters. Swarm Performance Indicators are defined here as Key Performance Indicators for swarm features but can be applied to multi-agent systems with centralised control as well. These swarm features are Robustness, Fault Tolerance, Adaptability and Scalability. Swarm Performance Indicators can be used to highlight the benefits of swarms beyond solely considering task-based performance metrics (e.g. time taken)