A New Multi-Agent System Consensus Algorithm Inspired by Synchronous Turtle Hatching Behavior

New technologies often rely on multi-agent systems (MASs) and their ability to achieve consensus. Current distributed consensus approaches, however, have narrow applicability and are only resilient to a small subset of faults. Biologically inspired design may provide the inspiration needed to develop new consensus algorithms to improve trust in autonomous systems, thus advancing the state of the art of systems engineering. Our hypothesis is that if the biological behavior of synchronous turtle hatching is evaluated, then a more resilient and novel consensus algorithm can be developed, because current turtle hatching requires robust consensus for species survival. This article presents the synchronous hatching consensus algorithm. To test the proposed algorithm, an agent-based, ANYLOGIC model was tested against 0, 1, 5, 10, 15, and 20 faulted agent(s) across four different environments. The time taken for 66% of the agents to accurately reach consensus about environmental conditions was recorded. There were 50 runs per number of faulted agents per environmental condition totaling 1200 runs. The consensus times were averaged and compared, to determine the impact of faulted agents and the environment on consensus time. The consensus time averages for the tests that consistently achieved the 66% consensus limit had coefficients of variance of 2.1%, showing resilience to faulted agents and proving that the proposed distributed consensus algorithm was resilient to faulted agents (even up to 20% of the population). Additionally, the results provide insight into the type of scenario the algorithm can be applied to (minimum viable parameter rate of change requirements).