Optimization of a matrix with integers with nonlinear objective fun... (2024)

GA Constrained optimization using genetic algorithm. GA attempts to solve problems of the following forms: min F(X) subject to: A*X <= B, Aeq*X = Beq (linear constraints) X C(X) <= 0, Ceq(X) = 0 (nonlinear constraints) LB <= X <= UB X(i) integer, where i is in the index vector INTCON (integer constraints) Note: If INTCON is not empty, then no equality constraints are allowed. That is:- * Aeq and Beq must be empty * Ceq returned from NONLCON must be empty X = GA(FITNESSFCN,NVARS) finds a local unconstrained minimum X to the FITNESSFCN using GA. NVARS is the dimension (number of design variables) of the FITNESSFCN. FITNESSFCN accepts a vector X of size 1-by-NVARS, and returns a scalar evaluated at X. X = GA(FITNESSFCN,NVARS,A,b) finds a local minimum X to the function FITNESSFCN, subject to the linear inequalities A*X <= B. Linear constraints are not satisfied when the PopulationType option is set to 'bitString' or 'custom'. See the documentation for details. X = GA(FITNESSFCN,NVARS,A,b,Aeq,beq) finds a local minimum X to the function FITNESSFCN, subject to the linear equalities Aeq*X = beq as well as A*X <= B. (Set A=[] and B=[] if no inequalities exist.) Linear constraints are not satisfied when the PopulationType option is set to 'bitString' or 'custom'. See the documentation for details. X = GA(FITNESSFCN,NVARS,A,b,Aeq,beq,lb,ub) defines a set of lower and upper bounds on the design variables, X, so that a solution is found in the range lb <= X <= ub. Use empty matrices for lb and ub if no bounds exist. Set lb(i) = -Inf if X(i) is unbounded below; set ub(i) = Inf if X(i) is unbounded above. Linear constraints are not satisfied when the PopulationType option is set to 'bitString' or 'custom'. See the documentation for details. X = GA(FITNESSFCN,NVARS,A,b,Aeq,beq,lb,ub,NONLCON) subjects the minimization to the constraints defined in NONLCON. The function NONLCON accepts X and returns the vectors C and Ceq, representing the nonlinear inequalities and equalities respectively. GA minimizes FITNESSFCN such that C(X)<=0 and Ceq(X)=0. (Set lb=[] and/or ub=[] if no bounds exist.) Nonlinear constraints are not satisfied when the PopulationType option is set to 'bitString' or 'custom'. See the documentation for details. X = GA(FITNESSFCN,NVARS,A,b,Aeq,beq,lb,ub,NONLCON,options) minimizes with the default optimization parameters replaced by values in OPTIONS. OPTIONS can be created with the OPTIMOPTIONS function. See OPTIMOPTIONS for details. For a list of options accepted by GA refer to the documentation. X = GA(FITNESSFCN,NVARS,A,b,[],[],lb,ub,NONLCON,INTCON) requires that the variables listed in INTCON take integer values. Note that GA does not solve problems with integer and equality constraints. Pass empty matrices for the Aeq and beq inputs if INTCON is not empty. X = GA(FITNESSFCN,NVARS,A,b,[],[],lb,ub,NONLCON,INTCON,options) minimizes with integer constraints and the default optimization parameters replaced by values in OPTIONS. OPTIONS can be created with the OPTIMOPTIONS function. See OPTIMOPTIONS for details. X = GA(PROBLEM) finds the minimum for PROBLEM. PROBLEM is a structure that has the following fields: fitnessfcn: <Fitness function> nvars: <Number of design variables> Aineq: <A matrix for inequality constraints> bineq: <b vector for inequality constraints> Aeq: <Aeq matrix for equality constraints> beq: <beq vector for equality constraints> lb: <Lower bound on X> ub: <Upper bound on X> nonlcon: <Nonlinear constraint function> intcon: <Index vector for integer variables> options: <Options created with optimoptions('ga',...)> rngstate: <State of the random number generator> [X,FVAL] = GA(FITNESSFCN, ...) returns FVAL, the value of the fitness function FITNESSFCN at the solution X. [X,FVAL,EXITFLAG] = GA(FITNESSFCN, ...) returns EXITFLAG which describes the exit condition of GA. Possible values of EXITFLAG and the corresponding exit conditions are 1 Average change in value of the fitness function over options.MaxStallGenerations generations less than options.FunctionTolerance and constraint violation less than options.ConstraintTolerance. 3 The value of the fitness function did not change in options.MaxStallGenerations generations and constraint violation less than options.ConstraintTolerance. 4 Magnitude of step smaller than machine precision and constraint violation less than options.ConstraintTolerance. This exit condition applies only to nonlinear constraints. 5 Fitness limit reached and constraint violation less than options.ConstraintTolerance. 0 Maximum number of generations exceeded. -1 Optimization terminated by the output or plot function. -2 No feasible point found. -4 Stall time limit exceeded. -5 Time limit exceeded. [X,FVAL,EXITFLAG,OUTPUT] = GA(FITNESSFCN, ...) returns a structure OUTPUT with the following information: rngstate: <State of the random number generator before GA started> generations: <Total generations, excluding HybridFcn iterations> funccount: <Total function evaluations> maxconstraint: <Maximum constraint violation>, if any message: <GA termination message> [X,FVAL,EXITFLAG,OUTPUT,POPULATION] = GA(FITNESSFCN, ...) returns the final POPULATION at termination. [X,FVAL,EXITFLAG,OUTPUT,POPULATION,SCORES] = GA(FITNESSFCN, ...) returns the SCORES of the final POPULATION. Example: Unconstrained minimization of Rastrigins function: function scores = myRastriginsFcn(pop) scores = 10.0 * size(pop,2) + sum(pop.^2 - 10.0*cos(2*pi .* pop),2); numberOfVariables = 2 x = ga(@myRastriginsFcn,numberOfVariables) Display plotting functions while GA minimizes options = optimoptions('ga','PlotFcn',... {@gaplotbestf,@gaplotbestindiv,@gaplotexpectation,@gaplotstopping}); [x,fval,exitflag,output] = ga(fitfcn,2,[],[],[],[],[],[],[],options) An example with inequality constraints and lower bounds A = [1 1; -1 2; 2 1]; b = [2; 2; 3]; lb = zeros(2,1); fitfcn = @(x)0.5*x(1)^2 + x(2)^2 -x(1)*x(2) -2*x(1) - 6.0*x(2); % Use mutation function which can handle constraints options = optimoptions('ga','MutationFcn',@mutationadaptfeasible); [x,fval,exitflag] = ga(fitfcn,2,A,b,[],[],lb,[],[],options); If FITNESSFCN or NONLCON are parameterized, you can use anonymous functions to capture the problem-dependent parameters. Suppose you want to minimize the fitness given in the function myfit, subject to the nonlinear constraint myconstr, where these two functions are parameterized by their second argument a1 and a2, respectively. Here myfit and myconstr are MATLAB file functions such as function f = myfit(x,a1) f = exp(x(1))*(4*x(1)^2 + 2*x(2)^2 + 4*x(1)*x(2) + 2*x(2) + a1); and function [c,ceq] = myconstr(x,a2) c = [1.5 + x(1)*x(2) - x(1) - x(2); -x(1)*x(2) - a2]; % No nonlinear equality constraints: ceq = []; To optimize for specific values of a1 and a2, first assign the values to these two parameters. Then create two one-argument anonymous functions that capture the values of a1 and a2, and call myfit and myconstr with two arguments. Finally, pass these anonymous functions to GA: a1 = 1; a2 = 10; % define parameters first % Mutation function for constrained minimization options = optimoptions('ga','MutationFcn',@mutationadaptfeasible); x = ga(@(x)myfit(x,a1),2,[],[],[],[],[],[],@(x)myconstr(x,a2),options) Example: Solving a mixed-integer optimization problem An example of optimizing a function where a subset of the variables are required to be integers: % Define the objective and call GA. Here variables x(2) and x(3) will % be integer. fun = @(x) (x(1) - 0.2)^2 + (x(2) - 1.7)^2 + (x(3) -5.1)^2; x = ga(fun,3,[],[],[],[],[],[],[],[2 3]) See also OPTIMOPTIONS, FITNESSFUNCTION, GAOUTPUTFCNTEMPLATE, PATTERNSEARCH, @. Documentation for ga doc ga

SURROGATEOPT The surrogateopt function is a global solver for time-consuming objective functions. surrogateopt attempts to solve problems of the form minimize f(x) subject to: lb <= x <= ub (bound constraints) A*X <= B, Aeq*X = Beq (linear constraints) c(x) <= 0 (nonlinear inequalities) x(i) must be integer for i in intcon The solver searches for the global minimum of a real-valued objective function in multiple dimensions, subject to bounds, optional integer constraints, and optional nonlinear inequality constraints. surrogateopt is best suited to objective functions that take a long time to evaluate. The objective function can be nonsmooth. The solver requires finite bounds on all variables. The solver can optionally maintain a checkpoint file to enable recovery from crashes or partial execution, or optimization continuation after meeting a stopping condition. x = SURROGATEOPT(fun,lb,ub) searches for a global minimum of fun(x) in the region lb <= x <= ub. fun accepts a single row vector argument x. fun can return either * a real scalar fval = fun(x), or * a structure. If the structure contains a field Fval, then surrogateopt attempts to minimize fun(x).Fval. If the structure contains a field Ineq, then surrogateopt attempts to make all of the components of that field be nonpositive: fun(x).Ineq <= 0 for all entries. For information on converting nonlinear constraints between the surrogateopt structure syntax and other solvers, see the packfcn function reference page. x = SURROGATEOPT(fun,lb,ub,intcon) requires that the variables listed in intcon take integer values. pass intcon = [] if there are no integer variables. x = SURROGATEOPT(fun,lb,ub,intcon,A,B) finds a local minimum x subject to the linear inequalities A*X <= B. x = SURROGATEOPT(fun,lb,ub,intcon,A,B,Aeq,Beq) finds a local minimum x subject to the linear equalities Aeq*X = Beq as well as A*X <= B. (Set A=[] and B=[] if no inequalities exist.) x = SURROGATEOPT(fun,lb,ub,intcon,A,B,Aeq,Beq,options) replaces the default optimization parameters by values in options. Create options using optimoptions. x = SURROGATEOPT(PROBLEM) searches for a minimum for PROBLEM, a structure with these fields: objective: The objective (and constraint) function lb: Lower bounds for x ub: Upper bounds for x intcon: Indices of variables that must be integer Aineq: <A matrix for inequality constraints> bineq: <B vector for inequality constraints> Aeq: <A matrix for equality constraints> beq: <B vector for equality constraints> rngstate: Optional field to reset the state of RNG options: Options created with OPTIMOPTIONS solver: 'surrogateopt' x = SURROGATEOPT(checkpointFile) continues running the optimization from the state in a saved checkpoint file. checkpointFile is a path to a checkpoint file, specified as a string or character vector. If you specify a file name without a path, SURROGATEOPT uses a checkpoint file in the current folder. SURROGATEOPT creates a checkpoint file when it has a valid CheckpointFile option. The data in a checkpoint file is in .mat format. To avoid errors or other unexpected results, do not modify the data before calling surrogateopt. x = SURROGATEOPT(checkpointFile,opts) continues running the optimization from the state in a saved checkpoint file, and replaces options in checkpointFile with those in opts. surrogateopt accepts changes in the following options: CheckpointFile Display MaxFunctionEvaluations MaxTime MinSurrogatePoints ObjectiveLimit OutputFcn PlotFcn UseParallel UseVectorized BatchUpdateInterval [x,fval] = SURROGATEOPT(__) also returns the best (smallest) value of the objective function found by the solver, using any of the input argument combinations in the previous syntaxes. [x,fval,exitflag] = SURROGATEOPT(__) also returns exitflag, an integer describing the reason the solver stopped. Possible values of exitflag and the corresponding exit conditions are listed below. 1 Best function value reached options.ObjectiveLimit 3 Feasible point but solver stopped (stalled) because no new points were found. 10 Unique solution exist for the problem due to one of the following reasons: All lower bounds are same as corresponding upper bounds. Unique solution due to linear equalities. 0 Number of function evaluations exceeded options.MaxFunctionEvaluations or elapsed time exceeded options.MaxTime. -1 Stopped by output/plot function. -2 No feasible point found due to one or more of the following reasons: One or more lower bound lb(i) exceeds a corresponding upper bound ub(i). One or more ceil(lb(i)) exceeds a corresponding floor(ub(i)) for i in INTCON. Linear constraints are infeasible. No solution found that satisfies nonlinear constraints. [x,fval,exitflag,output] = SURROGATEOPT(__) also returns output, a structure with these fields: rngstate: State of the random number generator before the solver started. funccount: Total number of function evaluations. message: Reason why SURROGATEOPT stopped. elapsedtime: Time spent running the solver in seconds, as measured by tic/toc. constrviolation: Maximum constraint violation due to nonlinear inequalities at x. ineq: Inequality constraints at the x. [x,fval,exitflag,output,trials] = SURROGATEOPT(__) also returns a structure containing all of the evaluated points (Npts) and the function function values at those points. trials have these fields: trials.X: Matrix of size Npts-by-Nvar. Each row of trials.X represents one point that SURROGATEOPT evaluated. trials.Fval: A vector, where each entry is the objective function value of the corresponding row of trials.X trials.Ineq: A matrix in which rows are nonlinear inequalties of the corresponding row of trials.X Examples: Find the minimum of a function subject to bounds. Search for a minimum of the six-hump camel back function in the region -2.1 <= x(i) <= 2.1. This function has two global minima with the objective function value -1.0316284... and four local minima with higher objective function values. fun = @(x)(4*x(:,1).^2 - 2.1*x(:,1).^4 + x(:,1).^6/3 ... + x(:,1).*x(:,2) - 4*x(:,2).^2 + 4*x(:,2).^4); lb = [-2.1,-2.1]; ub = -lb; x = surrogateopt(fun,lb,ub) Find the minimum of a function subject to nonlinear constraints. Find the minimum of Rosenbrock's function 100(x(2)-x(1)2)2+(1-x(1))2 subject to the nonlinear constraint that the solution lies in a disk of radius 1/3 around the point [1/3,1/3]: (x(1)-1/3)2+(x(2)-1/3)2 <= (1/3)2. To do so, write a function fun(x) that returns the value of Rosenbrock's function in a structure field Fval, and returns the nonlinear constraint value in the form c(x) <= 0 in the structure field Ineq. function f = fun(x) f.Fval = 100*(x(2) - x(1)^2)^2 + (1 - x(1))^2; f.Ineq = (x(1)-1/3)^2 + (x(2)-1/3)^2 - (1/3)^2; Call surrogateopt using lower bounds of 0 on each component and upper bounds of 2/3. lb = [0,0]; ub = [2/3,2/3]; [x,fval,exitflag] = surrogateopt(@fun,lb,ub) If objective and constraints are in separate function use this convenienece function to return output as a struct. objAndConstr = packfcn(@objective,@inequalities) X = surrogateopt(objAndConstr, ...) % objAndConstr is a function handle Find the minimum of a function subject to integer constraints Find the minimum of the piecewiseFcn function for a two-dimensional variable x whose first component is restricted to integer values, and all components are between -5 and 5. function f = piecewiseFcn(x) f = zeros(1,size(x,1)); for i = 1:size(x,1) if x(i,1) < -5 f(i) = (x(i,1)+5)^2 + abs(x(i,2)); elseif x(i,1) < -3 f(i) = -2*sin(x(i,1)) + abs(x(i,2)); elseif x(i,1) < 0 f(i) = 0.5*x(i,1) + 2 + abs(x(i,2)); elseif x(i,1) >= 0 f(i) = .3*sqrt(x(i,1)) + 5/2 +abs(x(i,2)); end end intcon = 1; rng default % For reproducibility fun = @piecewiseFcn; lb = [-5,-5]; ub = [5,5]; x = surrogateopt(fun,lb,ub,intcon) Restart from checkpoint file To enable restarting surrogate optimization due to a crash or any other reason, set a checkpoint file name. Create an optimization problem and set a small number of function evaluations. rng default % For reproducibility fun = @(x)(4*x(:,1).^2 - 2.1*x(:,1).^4 + x(:,1).^6/3 ... + x(:,1).*x(:,2) - 4*x(:,2).^2 + 4*x(:,2).^4); lb = [-2.1,-2.1]; ub = -lb; opts = optimoptions('surrogateopt','CheckpointFile','checkfile.mat'); opts.MaxFunctionEvaluations = 30; [x,fval,exitflag,output] = surrogateopt(fun,lb,ub,opts) Set options to use 100 function evaluations (which means 70 more than already done) and restart the optimization. opts.MaxFunctionEvaluations = 100; [x2,fval2,exitflag2,output2] = surrogateopt('checkfile.mat',opts) See also OPTIMOPTIONS, PATTERNSEARCH, FMINCON Documentation for surrogateopt doc surrogateopt