Are there examples of applications in blockchain consensus using swarm intelligence, as opposed to classical consensus mechanisms like PoW or PBFT?

Please note that recent classical consensuses, including lottery-based such as PoW in which the winner of lottery creates the new block, or voting-based such as PBFT or Paxos in which the entities achieve a consensus through a voting process; both approaches have problems of efficiency, latency, scalability, performance etc. And emerging a new alternative approach seems necessary. In this way, can nature-inspired algorithms (such as evolutionary algorithms (EA), particle swarm optimization (PSO), ant colony optimization (ACO) etc) be employed as an alternative to classical consensus algorithms?

  • $\begingroup$ I've been working on Proof of Reputation and variation on the "trust" mechanism in regards to the consensus problem. In trust/merit/reputation consensus swarm algorithms seem to be applicable to classification schemes and used similarly to Bayesian techniques, SVM, or linear regression. By running the chain transactions against some threshold there is some possibility that best fit membership can be found on the basis of trust or reputation. The research is unfinished, but promising. $\endgroup$ Oct 17 '20 at 15:57

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