www.gusucode.com > mbcdata 工具箱 matlab 源码程序 > mbcdata/mbcOSpsearch.m
function out = mbcOSpsearch(action, in)
%MBCOSPSEARCH CAGE Optimization library function
%
% MBCOSPSEARCH contains the configuration and evaluation routines for the
% CAGE Optimization, PATTERNSEARCH. This function is called by CAGE using
% the syntaxes below.
%
% OPTOBJ = MBCOSPSEARCH('OPTIONS', OPTOBJ) is called by CAGE when a
% PATTERNSEARCH optimization is created in CAGE. This call defines the
% structure of the optimization problem, using a CGOPTIMOPTIONS object.
% The completed configuration for the Optimization is returned to CAGE
% via OPTOBJ.
%
% OPTIMSTORE = MBCOSPSEARCH('EVALUATE', OPTIMSTORE) is called by CAGE
% when a user runs a PATTERNSEARCH optimization. Optimization parameters
% are passed to the evaluation routine through OPTIMSTORE. The results of
% the optimization are stored in the OPTIMSTORE object which is returned
% to CAGE for further processing.
%
% See also MBCOSNBI, MBCOSFMINCON, MBCOSGA
% Copyright 2006-2012 The MathWorks, Inc.
% Deal with the action inputs
if strcmp(action, 'options')
options = in;
% Add a name
options = setName(options, 'patternsearch');
% Add a description
options = setDescription(options, ...
'Single objective optimization using a pattern search algorithm');
% Allow objectives and constraints to be renamed
options = setRenameMode(options, true);
% Set up the operating point sets
%%%% Any number of operating point sets are allowed %%%%
options = setOperatingPointsMode(options, 'any');
% Determine whether Global Optimization is available
hasGADSToolbox = mbcCheckForToolbox(...
'Global Optimization', ...
'gads_toolbox', 'globaloptim');
options = setEnabled(options, hasGADSToolbox);
% Set up the optimization parameters
options = addParameter(options, 'Display', ...
{'list', {'none', 'final', 'iter', 'diagnose'}}, 'none');
options = addParameter(options, 'TimeLimit', ...
{'number', 'positive'}, Inf, 'Time limit');
options = addParameter(options, 'MaxIter', {'integer', 'positive'}, ...
100, 'Maximum number of iterations');
options = addParameter(options, 'MaxFunEvals', {'integer', 'positive'}, ...
100000, 'Maximum function evaluations');
options = addParameter(options, 'TolX', {'number', 'positive'}, ...
1e-6, 'Variable tolerance');
options = addParameter(options, 'TolFun', {'number', 'positive'}, ...
1e-6, 'Function tolerance');
options = addParameter(options, 'TolCon', {'number', 'positive'}, ...
1e-6, 'Constraint tolerance');
options = addParameter(options, 'TolMesh', {'number', 'positive'}, ...
1e-6, 'Mesh size tolerance');
options = addParameter(options, 'InitialMeshSize', ...
{'number', 'positive'}, 1, 'Initial mesh size');
options = addParameter(options, 'PollMethod', {'list', ...
{'GPSPositiveBasis2N', 'GPSPositiveBasisNp1', ...
'MADSPositiveBasis2N', 'MADSPositiveBasisNp1'}}, ...
'GPSPositiveBasis2N', 'Poll method');
options = addParameter(options, 'SearchMethod', {'list', ...
{'none', 'MADSPositiveBasisNp1', 'MADSPositiveBasis2N', ...
'GPSPositiveBasisNp1', 'GPSPositiveBasis2N', ...
'searchlhs', 'searchneldermead', 'searchga'}},...
'none', 'Search method');
out = options;
elseif strcmp(action, 'evaluate')
optimstore = in;
optimstore = i_Evaluate(optimstore);
out = optimstore;
else
error(message('mbc:mbcOSpsearch:InvalidArgument'));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function optimstore = i_Evaluate(optimstore)
% Ensure that Global Optimization is available
[canCheckOutTlbx, errmsg] = mbcCheckForToolbox(...
'Global Optimization', ...
'gads_toolbox', 'globaloptim', true);
if ~canCheckOutTlbx
error('mbc:mbcOSpsearch:InvalidState', errmsg);
end
% Set the functions
PollMethod = getParam(optimstore, 'PollMethod');
SearchMethodStr = getParam(optimstore, 'SearchMethod');
if strcmp(SearchMethodStr, 'none')
SearchMethod = [];
else
SearchMethod = str2func(SearchMethodStr);
end
% Set the other variables
Display = getParam(optimstore, 'Display');
TimeLimit = getParam(optimstore, 'TimeLimit');
MaxFunEvals = getParam(optimstore, 'MaxFunEvals');
TolX = getParam(optimstore, 'TolX');
TolFun = getParam(optimstore, 'TolFun');
TolCon = getParam(optimstore, 'TolCon');
TolMesh = getParam(optimstore, 'TolMesh');
MaxIter = getParam(optimstore, 'MaxIter');
InitialMeshSize = getParam(optimstore, 'InitialMeshSize');
% Get linear constraints
A = getA(optimstore);
B = getB(optimstore);
% Get the bounds and initial conditions. Transpose all vectors to make them
% consistent for scalar variables with patternsearch.
lb = getLB(optimstore);
ub = getUB(optimstore);
x0 = getInitFreeVal(optimstore);
% Put them all into a psoptions structure
options = psoptimset('Display', Display, 'TolFun', TolFun, ...
'TolCon', TolCon, 'TolMesh', TolMesh, 'MaxIter', MaxIter, ...
'InitialMeshSize', InitialMeshSize, 'PollMethod', PollMethod, ...
'SearchMethod', SearchMethod, 'CompletePoll', 'off', ...
'CompleteSearch', 'off', 'Cache', 'on', ...
'MeshAccelerator', 'on', 'Vectorized', 'on', ...
'PollingOrder', 'success', ...
'TimeLimit', TimeLimit, 'TolX', TolX, ...
'MaxFunEvals', MaxFunEvals, 'OutputFcns', @i_Output);
% Workaround issue in patternsearch. If all the bounds are infinite
% and A, B are empty, patternsearch will still pass to the bound
% constrained algorithm. In our view, it should pass to the
% unconstrained algorithm in this case.
if all(lb == -Inf)
lb = [];
end
if all(ub == Inf)
ub = [];
end
% Call optimization algorithm. Workaround issue in patternsearch. If a
% function handle to nonlinear constraints is specified then patternsearch
% will pass to the nonlinear constrained algorithm, even if the nonlinear
% constraint function returns no constraints. It should not pass to the
% nonlinear contrained algorithm in this case (it is currently much slower
% than the other algorithms).
[c, ceq] = i_evalCon(lb);
if isempty(c) && isempty(ceq)
[bestx, bestfun, exitflag, output] = patternsearch(@i_evalObj, x0, ...
A, B, [], [], lb, ub, [], options);
else
[bestx, bestfun, exitflag, output] = patternsearch(@i_evalObj, x0, ...
A, B, [], [], lb, ub, @i_evalCon, options);
end
% Write the results to optimstore. Transpose the free variables back to
% 1-by-NFREEVAR vector,
optimstore = setFreeVariables(optimstore, bestx);
% Set all the information in optimstore
optimstore = setExitStatus(optimstore, exitflag, output.message);
output = rmfield(output, 'message');
output = rmfield(output, 'function');
output = rmfield(output, 'searchmethod');
optimstore = setOutput(optimstore, output);
function y = i_evalObj(x)
% Evaluate the objective function. Transpose x as it is provided in
% the form NFREEVAR-by-NPTS for pattern search.
y = evaluateObjective(optimstore, x);
ObjectiveFuncTypes = getObjectiveType(optimstore);
switch ObjectiveFuncTypes{1}
case 'max'
y = -y;
case 'min'
otherwise
error('mbc:mbcOSpsearch:InvalidState', ...
strcat('This optimization routine can only deal with',...
'objective functions to be minimized or maximized'));
end
end
function [c, ceq] = i_evalCon(x)
% Nonlinear inequality constraints. Transpose x as it is provided in
% the form NFREEVAR-by-NPTS for pattern search.
c = evaluateNonlcon(optimstore, x);
% Nonlinear equality constraints currently not supported
ceq = [];
end
function [stop, options, optchanged] = i_Output(unused1, options, ...
unused2, unused3)
stop = getStopState(optimstore);
optchanged = false;
end
end