shu_fyh

求人来解答啊。。

feifeiyin12

可以实现，代码很简单，这个应该可以用非线性规划，随便找本matlab方面的书，半个小时可以搞定。手机不好发代码。你编程有问题可以去matlab论坛上问，那里这方面回答的人多些

shu_fyh

引用回帖:

feifeiyin12 at 2013-11-21 13:28:04

可以实现，代码很简单，这个应该可以用非线性规划，随便找本matlab方面的书，半个小时可以搞定。手机不好发代码。你编程有问题可以去matlab论坛上问，那里这方面回答的人多些

...

额，我说的是能不能用遗传算法搞定。。

月只蓝

引用回帖:

shu_fyh at 2013-11-21 14:08:03

额，我说的是能不能用遗传算法搞定。。...

可以用遗传算法来求解。

shu_fyh

引用回帖:

月只蓝 at 2013-11-21 16:06:11

可以用遗传算法来求解。...

遗产算法的过程我已经写出来了，就是不知道怎么用matlab来实现

月只蓝

引用回帖:

shu_fyh at 2013-11-21 16:53:14

遗产算法的过程我已经写出来了，就是不知道怎么用matlab来实现...

MATLAB有自带的遗传算法函数ga，虽然说MATLAB的内置的遗传算法并不是最优秀的，但求解你的问题应该没问题。

ga函数调用格式很方便，见MATLAB help：

GA    Constrained optimization using genetic algorithm.

GA attempts to solve problems of the form:

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 = 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 the

structure OPTIONS. OPTIONS can be created with the GAOPTIMSET function.

See GAOPTIMSET 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>

options: <Options structure created with GAOPTIMSET>

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.StallGenLimit generations less than options.TolFun and

constraint violation less than options.TolCon.

3 The value of the fitness function did not change in

options.StallGenLimit generations and constraint violation less

than options.TolCon.

4 Magnitude of step smaller than machine precision and constraint

violation less than options.TolCon. This exit condition applies

only to nonlinear constraints.

5 Fitness limit reached and constraint violation less than

options.TolCon.

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 'rastriginsfcn' fitness function of

numberOfVariables = 2

x = ga(@rastriginsfcn,2)

Display plotting functions while GA minimizes

options = gaoptimset('PlotFcns',...

{@gaplotbestf,@gaplotbestindiv,@gaplotexpectation,@gaplotstopping});

[x,fval,exitflag,output] = ga(@rastriginsfcn,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);

% Use mutation function which can handle constraints

options = gaoptimset('MutationFcn',@mutationadaptfeasible);

[x,fval,exitflag] = ga(@lincontest6,2,A,b,[],[],lb,[],[],options);

FITNESSFCN can also be an anonymous function:

x = ga(@(x) 3*sin(x(1))+exp(x(2)),2)

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 M-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 = gaoptimset('MutationFcn',@mutationadaptfeasible);

x = ga(@(x)myfit(x,a1),2,[],[],[],[],[],[],@(x)myconstr(x,a2),options)

，

木头919

在命令窗口输入gatool，调出工具箱，一目了然