Thanks John; I also really dislike all the mindless click-bait articles out there. This blog is all about content ðŸ™‚ - I really need to write more about neural networks though.

The basic idea is fairly straightforward. In nature (to a rough approximation), ants start out by walking in random directions looking for food, and as they do so, they deposit pheromones along their path. When an ant finds food, it will walk the same path repeatedly as it carries off as much as it can handle back to the colony, and then returns to the food source. When other ants are deciding where to travel, they prefer to follow routes where more pheromones have been deposited. Thus, more and more ants start walking the same route, which encourages more ants, until the whole colony is able to join in.

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where
τ
x
y
{\displaystyle \tau _{xy}}
is the amount of pheromone deposited for a state transition
x
y
{\displaystyle xy}
,
ρ
{\displaystyle \rho }
is the * pheromone evaporation coefficient* and
Δ
τ
x
y
k
{\displaystyle \Delta \tau _{xy}^{k}}
is the amount of pheromone deposited by
k
{\displaystyle k}
th ant, typically given for a TSP problem (with moves corresponding to arcs of the graph) by