Multi-objective optimization - Introduction 

This was a great video! As a mechanical engineer, the beam design optimization was a very good example for me. Thank you so much. 😊

Thank you so much ! I am teaching myself MOO to apply it to portfolio optimization problems and this was an incredibly helpful start

Excellent video. The last time I studied optimization techniques was in 2nd and 3rd year of electrical engineering and I graduated in 1999. So its been 20+ years since I studied this topic. HOWEVER, I have had a need to perform multi-level/objective optimizations in my business. This video was VERY HELPFUL in explaining the concepts quite clearly. Thank you!

30 minutes well spent. you took your time and i enjoyed the lecture suggestion: can you solve them using matlab? also can you introduce NGSA-1, NGSA-2 or NGSA-3 thank you so much

I wish the subtitles were active

Great video sir! highly informative and understandable !

very useful video for those who what to learn about MOO. Many many thank you. Could you please make a video of this example in the Excel.

Really clear and good explanation about Pareto Optimal Solution. 👍👍👍

good explanation. my dear friend, hope you do for BBO also. looking forward for your classes. my dear. thank you

I like your notation. Looking forward to watching the video.

This is really a good video. I would be thankful if can provide a video for proper pareto optimiality. Thanks in advance

what if there are more than two objective functions.Isn't it complex to decide the dominat and non dominant solutions?.

Many many thanks! It was very clear and useful!

Best ever found for MOO

sorry but which one of this is the dimension of solution ? n, M , J or K ?

Very well explained. Thanks a lot

Hi How i can select the best soulution from pareto front

A very good explanation. Could you please demonstrate a small implementation of multi-objective optimization?

Many thanks

Very useful, big thanks

Great video, I'm enriched.

Great lecture

Thank you

Thank you so much good sir

best explanation :)

Thank you