fgoalattain.m

遗传算法的小程序

function [x,FVAL,ATTAINFACTOR,EXITFLAG,OUTPUT,LAMBDA] = fgoalattain(FUN,x,GOAL,WEIGHT,A,B,Aeq,Beq,LB,UB,NONLCON,options,varargin)
%FGOALATTAIN Solves the multi-objective goal attainment optimization problem.
%
%   X = FGOALATTAIN(FUN,X0,GOAL,WEIGHT)
%   tries to make the objective functions (F) supplied by the function FUN
%   attain the goals (GOAL) by varying X. The goals are weighted according to
%   WEIGHT. In doing so the following nonlinear programming problem is solved:
%            min     { LAMBDA :  F(X)-WEIGHT.*LAMBDA<=GOAL } 
%          X,LAMBDA  
%
%   FUN accepts input X and returns a vector (matrix) of function values F 
%   evaluated at X. X0 may be a scalar, vector, or matrix.  
%
%   X=FGOALATTAIN(FUN,X0,GOAL,WEIGHT,A,B) solves the goal attainment problem
%   subject to the linear inequalities A*X <= B.
%
%   X=FGOALATTAIN(FUN,X0,GOAL,WEIGHT,A,B,Aeq,Beq) solves the goal attainment problem
%   subject to the linear equalities Aeq*X = Beq as well.  
%
%   X=FGOALATTAIN(FUN,X0,GOAL,WEIGHT,A,B,Aeq,Beq,LB,UB) defines a set of lower 
%   and upper bounds on the design variables, X, so that the solution is in 
%   the range LB <= X <= UB. Use empty matrices for LB and U if no bounds 
%   exist. Set LB(i) = -Inf if X(i) is unbounded below; set UB(i) = Inf if X(i) is 
%   unbounded above.
%   
%   X=FGOALATTAIN(FUN,X0,GOAL,WEIGHT,A,B,Aeq,Beq,LB,UB,NONLCON) subjects the 
%   goal attainment problem to the constraints defined in NONLCON (usually an 
%   M-file: NONLCON.m). The function NONLCON should return the vectors
%   C and Ceq, representing the nonlinear inequalities and equalities respectively, 
%   when called with feval: [C, Ceq] = feval(NONLCON,X). FGOALATTAIN 
%   optimizes such that C(X)<=0 and Ceq(X)=0.
%
%   X=FGOALATTAIN(FUN,X0,GOAL,WEIGHT,A,B,Aeq,Beq,LB,UB,NONLCON,OPTIONS) minimizes the 
%   with default optimization parameters replaced by values in the structure OPTIONS, 
%   an argument created with the OPTIMSET function.  See OPTIMSET for details.  Used
%   options are Display, TolX, TolFun, TolCon, DerivativeCheck, GradObj, GradConstr, 
%   MaxFunEvals, MaxIter, MeritFunction, GoalsExactAchieve, Diagnostics, DiffMinChange, 
%   and DiffMaxChange. Use the GradObj option to specify that FUN may be called with 
%   two output arguments where the second, G, is the partial derivatives of the
%   function df/dX, at the point X: [F,G] = feval(FUN,X). Use the GradConstr
%   option to specify that NONLCON may be called with four output arguments:
%   [C,Ceq,GC,GCeq] = feval(NONLCON,X) where GC is the partial derivatives of the 
%   constraint vector of inequalities C an GCeq is the partial derivatives of the 
%   constraint vector of equalities Ceq. Use OPTIONS = [] as a place holder if 
%   no options are set.
%
%   X=FGOALATTAIN(FUN,X0,GOAL,WEIGHT,A,B,Aeq,Beq,LB,UB,NONLCON,OPTIONS,P1,P2,...)
%   passes the problem-dependent parameters P1,P2,... directly to the 
%   functions FUN and NONLCON: feval(FUN,X,P1,P2,...) and feval(NONLCON,X,P1,P2,...).  
%   Pass empty matrices for A, B, Aeq, Beq, LB, UB, NONLCON and OPTIONS to use the 
%   default values.
%
%   [X,FVAL]=FGOALATTAIN(FUN,X0,...) returns the value of the objective 
%   function FUN at the solution X.
%
%   [X,FVAL,ATTAINFACTOR]=FGOALATTAIN(FUN,X0,...) returns the attainment factor
%   at the solution X. If ATTAINFACTOR is negative, the goals have been over-
%   achieved; if ATTAINFACTOR is positive, the goals have been under-achieved.
%
%   [X,FVAL,ATTAINFACTOR,EXITFLAG]=FGOALATTAIN(FUN,X0,...) returns a string 
%   EXITFLAG that describes the exit condition of FGOALATTAIN.  
%   If EXITFLAG is:
%      > 0 then FGOALATTAIN converged to a solution X.
%      0   then the maximum number of function evaluations was reached.
%      < 0 then FGOALATTAIN did not converge to a solution.
%   
%   [X,FVAL,ATTAINFACTOR,EXITFLAG,OUTPUT]=FGOALATTAIN(FUN,X0,...) returns a structure
%   OUTPUT with the number of iterations taken in OUTPUT.iterations, the number
%   of function evaluations in OUTPUT.funcCount, and the algorithm used in OUTPUT.algorithm.
% 
%   [X,FVAL,ATTAINFACTOR,EXITFLAG,OUTPUT,LAMBDA]=FGOALATTAIN(FUN,X0,...) returns 
%   the Lagrange multiplier at the solution X: LAMBDA.lower for LB, LAMBDA.upper 
%   for UB, LAMBDA.ineqlin is for the linear inequalities, LAMBDA.eqlin is for 
%   the linear equalities, LAMBDA.ineqnonlin is for the nonlinear inequalities, 
%   and LAMBDA.eqnonlin is for the nonlinear equalities.
%
%   For more details, type the M-file FGOALATTAIN.M.
%
%   See also OPTIMSET, OPTIMGET.

%   Copyright 1990-2002 The MathWorks, Inc. 
%   $Revision: 1.22 $  $Date: 2002/03/12 20:36:21 $
%   Andy Grace 7-9-90.


% ---------------------More Details---------------------------
% [x]=fgoalattain(F,x,GOAL,WEIGHT,[],[],[],[],[],[],[],OPTIONS)
% Solves the goal attainment problem where:
%
%  X  Is a set of design parameters which can be varied.
%  F  Is a set of objectives which are dependent on X.
%  GOAL Set of design goals. The optimizer will try to make 
%         F<GOAL, F=GOAL, or F>GOAL depending on the formulation.
%  WEIGHT Set of weighting parameters which determine the 
%         relative under or over achievement of the objectives.
%         Notes:
%           1.Setting WEIGHT=abs(GOAL)  will try to make the objectives
%             less than the goals resulting in roughly the same 
%             percentage under or over achievement of the goals.
%             Note: use WEIGHT 1 for GOALS that are 0 (see Note 3 below).
%           2. Setting WEIGHT=-abs(GOAL) will try to make the objectives
%              greater then the goals resulting in roughly the same percentage 
%              under- or over-achievement in the goals.
%             Note: use WEIGHT 1 for GOALS that are 0 (see Note 3 below).
%           3. Setting WEIGHT(i)=0  indicates a hard constraint.
%              i.e. F<=GOAL.
%  OPTIONS.GoalsExactAchieve indicates the number of objectives for which it is
%      required for the objectives (F) to equal the goals (GOAL). 
%      Such objectives should be partitioned into the first few 
%      elements of F.
%      The remaining parameters determine tolerance settings.
%          
%
%
defaultopt = struct('Display','final',...
   'TolX',1e-6,'TolFun',1e-6,'TolCon',1e-6,'DerivativeCheck','off',...
   'Diagnostics','off',...
   'GradObj','off','GradConstr','off','MaxFunEvals','100*numberOfVariables',...
   'MaxIter',400,...
   'MaxSQPIter',Inf,...
   'Hessian','off','LargeScale','off',... 
   'DiffMaxChange',1e-1,'DiffMinChange',1e-8, 'MeritFunction','multiobj',...
   'GoalsExactAchieve', 0);
% If just 'defaults' passed in, return the default options in X
if nargin==1 & nargout <= 1 & isequal(FUN,'defaults')
   x = defaultopt;
   return
end

caller='fgoalattain';

if nargin < 12, options = [];
   if nargin < 11, NONLCON = [];
      if nargin < 10, UB = [];
         if nargin < 9, LB = [];
            if nargin < 8, Beq = [];
               if nargin < 7, Aeq = [];
                  if nargin < 6, B = [];
                     if nargin < 5, A = [];
                        if nargin < 4, error('fgoalattain requires four input arguments'); 
                        end,end,end,end,end,end,end,end,end 
                        

xnew=[x(:);0];

numberOfVariablesplus1 = length(xnew);
numberOfVariables = numberOfVariablesplus1 - 1;
WEIGHT = WEIGHT(:);
GOAL = GOAL(:);

diagnostics = isequal(optimget(options,'Diagnostics',defaultopt,'fast'),'on');
switch optimget(options,'Display',defaultopt,'fast')
case {'off','none'}
   verbosity = 0;
case 'iter'
   verbosity = 2;
case 'final'
   verbosity = 1;
otherwise
   verbosity = 1;
end

% Set to column vectors
B = B(:);
Beq = Beq(:);

[xnew(1:numberOfVariables),l,u,msg] = checkbounds(xnew(1:numberOfVariables),LB,UB,numberOfVariables);
if ~isempty(msg)
   EXITFLAG = -1;
   [FVAL,ATTAINFACTOR,OUTPUT,LAMBDA] = deal([]);
   x(:) = xnew(1:numberOfVariables);
   if verbosity > 0
      disp(msg)
   end
   return
end

neqgoals = optimget(options, 'GoalsExactAchieve',defaultopt,'fast');
% meritFunctionType is 1 unless changed by user to fmincon merit function;
% formerly options(7)
% 0 uses the fmincon single-objective merit and Hess; 1 is the default
meritFunctionType = strcmp(optimget(options,'MeritFunction',defaultopt,'fast'),'multiobj');

lenVarIn = length(varargin);
% goalgra and goalfun also take: neqgoals,funfcn,gradfcn,WEIGHT,GOAL,x
goalargs = 6; 

gradflag = strcmp(optimget(options,'GradObj',defaultopt,'fast'),'on');
gradconstflag = strcmp(optimget(options,'GradConstr',defaultopt,'fast'),'on');
hessflag = strcmp(optimget(options,'Hessian',defaultopt,'fast'),'on');
if hessflag
   warning('FGOALATTAIN does not use user-supplied Hessian.')
   hessflag = 0;
end

if isempty(NONLCON)
   constflag = 0;