Non-evolutionary optimization

[alexandre.flamant]

@Dani_Abalde

There is a simpler way to approximate the gradient of any continuous function: F(x+h) - F(x), where h is a value close to 0. Given a fitness function, you evaluate it in x in at x+h, and you can measure how it will change. If the fitness decrease, then move x backwards, if x+h is more apt, move it forward, iteratively until it converges into an acceptable error.
Here is a quick implementation using Anemone. Now I don’t have time to make it robust, but the main idea is there.

Thank you very much ! I didn’t know it was possible to create loop, as I’ve been facing that circular references lead to errors !

@ParamDesSing

Specifically, for this type of “convex” problem, the local BOBYQA and COBYLA in Goat should work very well.

I can confirm by trial and error, BOBYQA and COBYLA are the two best options !

@DanielPiker

If you have examples where you’re trying to use Kangaroo and aren’t getting the results you expect, you can post them here. Often form-finding tensegrity structures can be as simple as modelling the approximate geometry, then assigning a pre-tension to the cables and making the struts stiff. If you are starting very far from an equilibrium position though you might need a bit more.

I’m actually struggling a bit to create an efficient script that create assembly of tensegrity cells …
I’m trying to vary the thickness of the assembly as well as the height thus leading to a specific ratio between compressed and tensile elements for each level of the assembly

I obtain some results by using a unique big ratio, but it makes the structure kind of inflating … Ideally, the best way to achieve good optimization of ratio would be maximizing the length of compressed element (in red in the script) with respect to the length goals of the other elements. But I can’t find a way to do it …

Node on node non-regular tensegrity.gh (25.9 KB)