Single objective optimization design space exploration


This question is not about Wallacei exactly, but as I do not know of a better place to post this question I might as well try here.

I am working on a project about the optimization of a shear wall layout. The optimised objective is mass. However, there might be other reasons (architectural, technical) why the optimum solution should not be chosen, which cannot be inserted into the optimization algorithm. Therefore it would be great if you could explore a wider design space and find other distinct solutions which are near optimal.

Does this seem like a possible or/and a smart thing to do with genetic optimziation algorithms? Does anyone have any examples where this sort of design space exploration has been implemented before? From a quick google search all of the results cover multi objective optimization and the pareto front, which is a bit of a different thing. The closest thing I’ve found is this program by Renaud Danhive - Renaud Danhaive

Many thanks in advance!

Hi Matthias,

This is possible in Wallacei. One of the key advantages of Wallacei is that it gives you access to the phenotypes of ALL the solutions in the population. In tabs 2 and 3 of the wallacei user interface, you can choose any solution (not only from the pareto front) to export. You can also take advantage of the Wallacei analytics components to explore all the solutions as well and find the one most suitable to you.