I second @flourishomotola5306, there are paid videos FAR from the quality of this content. Straight to the point, clear, well explained, and high quality content overall. Thanks @OpenMDAO
secondly if you find yourself optimizing multiple outcomes you might want to think about your optimization space. for example: maybe you dont want to optimize fuel burn and zero-fuel weight but want to optimize ROI on that plane for your given business model. answering that question will give you weights or a greater function that you might want to optimize.
Hi sir, for the study i am conducting right now in high school, i have only one decision variable that correlates to two conflicting functions that i want to minimize. this means the objective space would only have a Pareto front and not any "surfaces" of different combinations of the two objectives, because defining the value of one function immediately locks my only variable and hence the second function as well. my question is... in such cases, is it reasonable to approach these situations with the same MOO techniques? or if not, could you please guide me to some other ways that I can achieve such MOO with only one decision variable? Thanks in advance.
Thanks for explaining your situation well! This is a sort of special case because you have only one design variable and two functions of interest. If you only have one design variable, you're pretty limited in how much optimization you can do in that space. If possible, reformulating your problem to have more design freedom would be helpful, otherwise there's not much to do to explore the space.
That's another way to combine objectives. What that means is that each objective would be weighted by the current value of the other, whereas adding them together assumes no such weighting. The most common type of multiobjective optimization adds the objectives together with different pre-defined weights, as shown in the latter part of this lesson.
![NSGA-II Optimization: Understand fast how it works [complete explanation]](/img/1.gif)
Thank you so much for an incredible work you have created.
This video saved me. thank you so much ❤
for weighted sum method how do u determine the scale factor. is it max of that value.?
How is this video free? Thanks a lot. Very clear and concise.
I second @flourishomotola5306, there are paid videos FAR from the quality of this content. Straight to the point, clear, well explained, and high quality content overall. Thanks @OpenMDAO
Thank you! Comments like this really propel us!
secondly if you find yourself optimizing multiple outcomes you might want to think about your optimization space. for example: maybe you dont want to optimize fuel burn and zero-fuel weight but want to optimize ROI on that plane for your given business model. answering that question will give you weights or a greater function that you might want to optimize.
Great Video! thank you so much...................
Hi sir, for the study i am conducting right now in high school, i have only one decision variable that correlates to two conflicting functions that i want to minimize. this means the objective space would only have a Pareto front and not any "surfaces" of different combinations of the two objectives, because defining the value of one function immediately locks my only variable and hence the second function as well.
my question is... in such cases, is it reasonable to approach these situations with the same MOO techniques? or if not, could you please guide me to some other ways that I can achieve such MOO with only one decision variable? Thanks in advance.
Thanks for explaining your situation well! This is a sort of special case because you have only one design variable and two functions of interest. If you only have one design variable, you're pretty limited in how much optimization you can do in that space. If possible, reformulating your problem to have more design freedom would be helpful, otherwise there's not much to do to explore the space.
wait a second, my intuition would be not to add the variables but to multiply them. am i wrong?
That's another way to combine objectives. What that means is that each objective would be weighted by the current value of the other, whereas adding them together assumes no such weighting. The most common type of multiobjective optimization adds the objectives together with different pre-defined weights, as shown in the latter part of this lesson.
0:01 you won me here k
+1 subscriber
that shit was so annoying.