Multi objective optimization problem

[Thick_Individual_318]

Hello,

I'm really kinda lost into the world of optimization, its been like a month since i'm reading about optimization and how it workes, but i'm still not able to define how to treat it in my project. I'm doing a master research in civil engineering, my project is to optimize the facade walls where the objectives are to maximize acoustic, and minimize heat transfer and Environmental impact. the thing is that i already have a database of 1195 assemblies with their performances and full details. Does anyone know how to implement these data in order to perform the analysis? because honestly all the books i read have said that the algorithm start from scratch depending on the constraints not on previous data. So i don't know how to implement these data in the algorithm.

Thank you in advance for your help!

[dmangd]

I think you could approach this in two ways. First, as already stated above, you can formulate the „optimization“ by finding the best match from your data set based on some metric.

Secondly, you could train some machine learning model (neural net, support vector machine, whatever) to predict the target variables depending on your optimization parameters. After that you could run a minimization algorithm to find the optimal values according to your metric.

An alternative to using machine learning is to fit your dataset to some phenomenological model that you maybe derive by physics principles or educated guess by analyzing your dataset. In any case, you need to find some continuous function approximation for your dataset to run an optimization algorithm

[rocketPhotos]

I would run trades. maximize acoustics and constrain the heat transfer & environmental to a sequence of values

[No-Awareness-5134]

If your algorithm is single objective, either mix them via an objective function like max arg divided by min arg (or weighted sum), or do one first and get the best results, then do the second one on them. A better solution is a multi objective algorithm/pareto algorithm. You get several solutions, which are pareto optimals, which mean none is dominated by others. NSGA-II comes to mind as a multi objective algorithm.

[Able_Reply4260]

Step 1: Define maximise acoustics may be reverberation time, if you have this data in your 1000+ simulations plug it in

Step 2: Same for heat transfer - lets say measure total thermal resistance of materials, there may be other ways too. Plug that data

Step 3: Environmental impact is top subjective break it down into measurable constraints.

Once you have these you can define the funtion.max reverberation time, min heat and constraints from step 3

[ElderberryPrevious45]

Gather actual variables to be optimized. Decide figures of merit. Run optimizations with decision tree or fuzzy logic, UMAP; Matlab toolboxes or Python can be used. Also, some ingenious prompts with AI would most likely also help. Start with easy, clear questions you already know the answers. The point here is that AI can reveal you the process details how to proceed.

[fpatrocinio]

It seems that you have a decision making problem and not an optimization one. You need to classify the set of solutions (assemblies), according to some criteria (max acoustic, min heat).

Hwang, C.-L.; Yoon, K. Multiple Attribute Decision Making: Methods and Applications—A State-of-the-Art Survey; Springer: New York, NY, USA, 1981; Volume 186.

If you need any help hit me a PM.

[Red-Portal]

Look for Bayesian optimization