## Robust optimization problem

## Robust optimization problem

(OP)

Hi,

For a measurement set-up I'm designing, I have to position two sensors which have to be placed at an angle (alpha, beta) wrt the x-direction (zero degrees). However, there is only one 'optimal' combination of angles (alpha-beta) in words of robustness. In order to determine this 'optimum', I want to do a sensitivity analysis how each possible combination of angles is sensitive to a small (angular) deviation i.e. in practice it is impossible to locate the two sensors perfect at the desired location. The combination of angles that is less sensitive to this variation of location is the optimal location I'm searching.

I searched on the internet which numerical method to use for this, however I'm not really experienced in optimization and stuff like that. I found methods like Monte Carlo, perturbation analyses, Taguchi etc. I'm reading already a several days but still I'm not sure which method to use for my problem. Anybody some suggestions which method is most appropriate? Examples (Matlab), tutorials etc.?

Any help or suggestion is welcome!!

Thanks

For a measurement set-up I'm designing, I have to position two sensors which have to be placed at an angle (alpha, beta) wrt the x-direction (zero degrees). However, there is only one 'optimal' combination of angles (alpha-beta) in words of robustness. In order to determine this 'optimum', I want to do a sensitivity analysis how each possible combination of angles is sensitive to a small (angular) deviation i.e. in practice it is impossible to locate the two sensors perfect at the desired location. The combination of angles that is less sensitive to this variation of location is the optimal location I'm searching.

I searched on the internet which numerical method to use for this, however I'm not really experienced in optimization and stuff like that. I found methods like Monte Carlo, perturbation analyses, Taguchi etc. I'm reading already a several days but still I'm not sure which method to use for my problem. Anybody some suggestions which method is most appropriate? Examples (Matlab), tutorials etc.?

Any help or suggestion is welcome!!

Thanks

## RE: Robust optimization problem

If you don't know how one factor affects the rest, simulation of any kind is useless. Time to hit the lab bench.

Dan - Owner

http://www.Hi-TecDesigns.com

## RE: Robust optimization problem

TTFN

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## RE: Robust optimization problem

## RE: Robust optimization problem

Dan - Owner

http://www.Hi-TecDesigns.com

## RE: Robust optimization problem

TTFN

FAQ731-376: Eng-Tips.com Forum Policies

Chinese prisoner wins Nobel Peace Prize

## RE: Robust optimization problem

## RE: Robust optimization problem

Then since you are not familiar with optimization problems then I suggest using Matlabs built in optimization solvers like http://www.mathworks.com/products/optimization/

or just type fmin.

On the other hand. in my experience in optimization, I generally use the sequential simplex method for non-global problems. And recently I programed the particle swarm optimization (PSO) and found it to be very exceptional at global type problems. I highly suggest it.

Just find a model and you are good.

Fe

## RE: Robust optimization problem

Fe

## RE: Robust optimization problem

A Genetic Algorithm usually gives you the result faster, especially if you have many variables to deal with.

## RE: Robust optimization problem

as you have an physical experiment, not an mathematical model, You will need to use experimental suited optimisation technique. The problem is with proper preparation of the experiment table. This is what he mean by DOE. For this You need to consider all your inputs and ouputs. There could be some additional input parameters according to what you are measuring (i.e. distance), and additional output parameters associated to sensors (i.e. environment conditions)

Then try to determine probability of exact placement of each sensor its standard deviation (trial and error method can be used for n trials where n should be more than > 3.8416*EstimatedStDev^2/AssumedError^2)