The idea is simple. Ants travel along different paths towards their food source and release pheromones on the way. Other ants and/or the same ant then follow that pheromone trail. The pheromone decay is a time-dependent function, thus the concentration of pheromones is greatest along the shortest path. Most of the ants travel along the most frequently used path.

I have no ideas as of know so please brainstorm below, I just got reminded of this concept a few days ago.

Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.

One variant of this is the bees algorithm!

I like stretching metaphors till they break, so what would 1Hive be optimizingâŚ It has graphs. SourceCred is a graph. Honeyswapâs transaction history could be put into a graph. Indeed, companies do this with payment data to do fraud detection, among other analysis. On-chain transactions could be put in a graph. Thatâs the basis for chainanalysis.

But what would you optimize? The only thing that comes to mind is the arbitrage bots running on Honeyswap. As explained when I asked about micro transactions I was seeing that didnât make any sense, arbitrage bots will hop through different pools taking advantage of âtriangular arbitrageâ opportunities. Perhaps like ants, bots could crawl through Honeyswap searching for pools of liquidity? I suspect notâŚas calculating arbitrage opportunties can probably done via brute force? Presumably the shortest distance to liquidity is easy to calculate deterministically? Anyway, fun to think about.