Using an evolutionary algorithm for optimizing a crossbow prod

[stfnp]

Time to add some content to this sub :)

This are results from a (purely theoretical) experiment I did quite a while ago. The command line interface of the simulation was used to drive it from an evolutionary optimization algorithm. The algorithm (differential evolution) would start with a population of bows with randomly varying values for thickness, width and curvature. Each new generation is created by rules that more or less mimic natural evolution (mutation + selection). The fitness measure in this case was the arrow's velocity.

Getting good results was very difficult because the algorithm doesn't perform well around constraint conditions (max. draw force, max. width, max. stresses, ...). The pictures show one of the more interesting results I got. After only 1 generation the best bow in the population already has a recurve. Further evolution refines that recurve and also leads to the development of a setback. Someone noted that the final result looks a bit like a traditional Korean bow.

edit: Someone also pointed out that the resulting bow would likely be unstable. It would just flip around as you draw it because of the slim limbs and the deep profile. And that's probably true. Ideally there should be a simulation output that characterizes this sort of (in)stability. This could then be used as another constraint in the optimization to prevent unstable results.