Introduction to Scalarization Methods for Multi-objective Optimization

[@edgarduarte6926]

An excelent video with clear explanations. Thanks a lot for your contribution. Please, let us know if you are planning to post more videos like this.

[@Reach2Sabya]

Extremely helpful. Thank you.

[@TechyonChronosx]

SO HELPFULLL LOVE U !

[@hanumanatonu]

Hello Sir, I learnt a lot from this lecture. Thanks a lot for the video. I have used the weighted sum method for ranking my alternatives of products from which I am choosing the products based on the ranking achieved by the weighted sum method. I have 6 parameters for which I have assigned weights. Now I need to prove that the solution is convex. Could you please guide me on this? How do I proceed to solve that the ranking I have done is optimal?

[@eda7210]

Thank you so much

[@farmad100]

a very good lecture. please post video on multiobjective optimization thru gravitation search algo (meta heuristic) with matlab code

[@farmad100]

Dear sir can you please post a video on how to use multiple response optimization in formulation of single objective function

[@youssefelamrani7905]

Good Job, I have a question in the example you gave, what exactly is the solution? all we got at the end was a graph of mu and mu, Can you please elaborate on that? Thank You

[@designimpact2178]

The solution to a multi-objective optimization problem is a set of non-dominated points. The plot shows the set of points that comprise the solution.

[@mohamedharidy2200]

hanks a lot, is it possible to share the slides' file?

[@jamesallison753]

Thank you for asking. Right now I only make slides available to students who enroll in my class or training workshops.