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Particle Swarm Optimization info

Marco Marchese さんによって質問されました 2018 年 12 月 27 日
最新アクティビティ Marco Marchese さんによって コメントされました 2019 年 1 月 28 日
I am trying to find the values of 19 free variables from my data, using the psw optimization algorithm. I got interest in seeing the evolution of the 19 variables together with the minimization of the objective function, over the iterations. I then want to compare this againnst the genetic algorithm.
with the genetic algorithm I am able to to so, and get information (see code below), but with the psw I am no cabable of doing it. With the ga, I call this function from gaoptimset('PlotFcns',{@gaplotbestf, @gapop_func},...
Can you help me with this? Thank you a lot
function [state,options,optchanged] = gapop_func(options,state,flag)
persistent best r history_min history_max %h1
optchanged = false;
switch flag
case 'init'
best = state.Population;
assignin('base','gapopbestx',best);
case 'iter'
ibest = state.Best(end);
ibest = find(state.Score == ibest,1,'last');
bestx = state.Population(ibest,:);
best = [best; bestx];
assignin('base','gapop_max_best',history_max);
case 'done'
assignin('base','gapopbestx',best);
end

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1 件の回答

Alan Weiss
回答者: Alan Weiss
2018 年 12 月 28 日
 採用された回答

The syntaxes for output functions differ between ga and particleswarm. For an example of a particleswarm output function, see this example. For the correct way to set options for particleswarm, see the same example (you must use optimoptions to set options).
Alan Weiss
MATLAB mathematical toolbox documentation

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Alan Weiss
2019 年 1 月 3 日
Allow me to make one further observation. Generally, patternsearch is the most reliable and fastest-converging solver for nonsmooth problems. Please be sure to compare with it. And, if your problem is smooth, you should use fmincon instead. To search for a global solution using fmincon, try MultiStart. To search for a global solution using patternsearch, try uniformly distributed initial points:
x0 = lb + rand(size(lb)).*(ub - lb);
Good luck,
Alan Weiss
MATLAB mathematical toolbox documentation
Marco Marchese 2019 年 1 月 7 日
Thank you for the suggestion. The issue is that my function is quite complex and the computational time is very extended, thus I do not see much difference between G.A., PSO, Patternsearch. I have a non-smooth, non-linear function with a set of unknown values that i need to fit from experimental data.
Marco Marchese 2019 年 1 月 28 日
Apparently both patternsearch and PSO work out fine.
Is there a way I can extract confidence intervals from here? I would want to avoid to pass through nlingit,lsqcurvefit or lsqnonlin.

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