passing variable through pattern search iterations

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Andrea Agosti
Andrea Agosti 2020-3-30
编辑： Venus liria silva mendes 2021-5-5
Hi everyone!
I'm using pattern search to solve a minmax problem. I know that pattern search:
1) Starts witha a polling phase where it polls the points in the current mesh by computing their objective function values,
2) it groups all the values of the objective functions and it select the mesh case with highest objective function value,
3) it moves the mesh in the last successful poll point (or it leaves the central mesh point as before) and starts again from 1),
4) this continues untill convergence is reached (possibly).
My question is: Is it possible to pass a variable from the best objective function (point 2) to the next polling phase (point 3)?
Many thanks!
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Andrea Agosti
Andrea Agosti 2020-3-31
Dear Ameer,
thanks for your answer. Yes you understood correctly, between each iteration of the pattern search I want to be able to read with the value of the objective function, also another variable. This variable will be later passed for the next iteration of pattern search.
Thanks for your help
Venus liria silva mendes
Venus liria silva mendes 2021-5-4
编辑：Venus liria silva mendes 2021-5-5
Hi everyone
%% Modify options setting
my example problem:
[combination, custototal, exitFlag, Output, population, scores] = ga (@ smc09v7AG_01, n_vars, A, b, Aeq, beq, LB, UB, NON_linear, Integral_variables, settings)
'' population '' I'm not sure if all individuals from all generations or just the last one return. And the "scores" returns the evaluations of each one.
Hope it works!
https://www.mathworks.com/help/gads/genetic-algorithm-options.html

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Ameer Hamza
Ameer Hamza 2020-3-31
Following code shows how to get the information from each iteration of patternsearch
global x_iterations y_iterations
x_iterations = [];
y_iterations = [];
obj_fun = @(x) sum(x.^2.*exp(x.^2).*abs(log(x+1)));
opts = optimoptions('patternsearch', 'OutputFcn', @myOutFcn);
[x_final, f_final] = patternsearch(obj_fun, rand(1,10), [], [], [], [], [], [], [], opts);
function [stop, options, optchanged] = myOutFcn(optimvalues, options, flag)
global x_iterations y_iterations
x_iterations = [x_iterations; optimvalues.x];
y_iterations = [y_iterations; optimvalues.fval];
stop = false;
optchanged = false;
end
This page show how to define the outputFcn to get more detail for each iteration of the optimization algorithm: https://www.mathworks.com/help/gads/pattern-search-options.html#f14623
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Zakaria
Zakaria 2020-4-6
Does this methodology work with Genetic Algorithm optimizioation ?
I noticed that the structure of the OutputFcn is not the same.
Ameer Hamza
Ameer Hamza 2020-4-6
Yes, it is different. Please check my answer on your question.

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