EDIT: In lieu of other suggestions, I’ve decided to play around with the Python DEAP framework until I come up with something usable. Hopefully this helps people with a similar problem.
I am implementing a system in which we need to do multi-objective optimization for a client, as follows:
A manufacturing system has to produce N parts (of the same kind) by welding. For this, a certain material can be chosen, a welding method, and the number of welding points to use for building each part. The image below shows the parameters of the problem and their interdependencies:
I can play with values for
material
welding method
number of spot welding points
I need to find the combination of material, welding method and number of welding points/part that minimizes the cost and maximizes the stability.
I was thinking of using an evolutionary algorithm approach.
However, my background is not in optimization, so, if someone could suggest a more specific algorithm that’s suited for this problem, it would be really helpful.
Assuming that you have a cost function for ‘cost’ and ‘stability’ you can use a traditional multi-objective EA like NSGa-II, SPEA-2 and maybe PAES. Obviously, the choice of the algorithm is strongly dependent on the number of samples do you have, on the features of cost functions and other important characteristics. You can check on journals like Applied Soft Computing for similar applications.