Error using fmincon Too many input arguments
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Hello , I have an emargency problem as the due date of my project is close. while every things is correct I dont know why when I run my code, it gives me an error of too many input arguments.
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% Wheelchair
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function Wheelchair
% clc;
clear all; close all;
dp=design_parameters; % Generate structure variable containing all design parameters
f=@(x)objective_function(x,dp); % Objective function handle
nonlcon=@(x)nonlinear_constraints(x,dp); % Nonlinear constraint function handle
lb=[dp.X1_min;dp.X2_min;dp.X3_min;dp.X4_min];% Design variable bounds
ub=[dp.X1_max;dp.X2_max;dp.X3_max;dp.X4_max]; % Design variable bounds
x0=[25e-3;1.5e-3;20e-3;1e-3]; % Initial design point
A=[0,-(0.68*dp.db*dp.sigmau)/(0.3*dp.mb*dp.g),0,0;-1/dp.X1_min,0,0,0;1/dp.X1_max,0,0,0;0,-1/dp.X2_min,0,0;0,1/dp.X2_max,0,0;0,0,-1/dp.X3_min,0;0,0,1/dp.X3_max,0;0,0,0,-1/dp.X4_min;0,0,0,1/dp.X4_max];
b=[-1,-1,1,-1,1,-1,1,-1,1];
% Minimization using fmincon and the specified options:
options=optimoptions('fmincon','Display','iter','OptimalityTolerance',1e-12,'ConstraintTolerance',1e-6,'StepTolerance',1e-12);
[x,fval]=fmincon(f,x0,A,b,[],[],lb,ub,nonlcon,options);
% Display of the optimal design results:
disp(['Optimal value of d1: ',num2str(x(1)),' mm']);
disp(['Optimal value of t1: ',num2str(x(2)),' mm']);
disp(['Optimal value of d2: ',num2str(x(3)),' mm']);
disp(['Optimal value of t2: ',num2str(x(4)),'mm']);
disp(['Optimal value of f: ',num2str(fval),' kg']);
end
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function dp=design_parameters
dp.L1=0.82; %(m)
dp.L2=0.691; %(m)
dp.L3=0.270; %(m)
dp.L4=0.04; %(m)
dp.L5=1.59; %(m)
dp.L6=0.627; %(m)
dp.L7=0.568;
dp.Lad=0.601;
dp.Lss=0.4;
dp.mb=150;
dp.Rho=2700;
dp.g=10;
dp.db=6e-3;
dp.sigmau=110e6;
dp.E=70e9;
dp.K=2.1;
dp.deltamax=1e-3;
dp.X1_min=20e-3; dp.X1_max=30e-3; %
dp.X2_min=1e-3; dp.X2_max=2e-3; %
dp.X3_min=1e-3; dp.X3_max=22e-3; %
dp.X4_min=0.8e-3; dp.X4_max=1.5e-3;
end
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function f=objective_function(x,dp)
A1=(x(1)/2)^2-(x(1)/2-x(2))^2;
A2=(x(1)/2-x(2))^2-(x(1)/2-2*x(2))^2;
A3=((x(1)+2*x(2))^2-(x(1))^2)/4;
A4=(x(3)/2)^2-(x(3)/2-x(4))^2;
f=2*((pi*A1*dp.L1*dp.Rho)+(pi*A1*dp.L2*dp.Rho)+(pi*A2*dp.L3*dp.Rho)+(6*pi*A3*dp.L4*dp.Rho)+(0.097))+((pi*A4*dp.L5*dp.Rho)+(pi*A4*dp.L6*dp.Rho)+(pi*A4*dp.L7*dp.Rho)); %
end
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function [c,ceq]=nonlinear_constraints(x,dp)
A5=(x(1))^4-(x(1)-2*x(2))^4;
c(10,1)=0.2*dp.mb*dp.g-((pi.^3* dp.E/(dp.K*dp.Lad)^2*64)*A5); %
c(11,1)=((dp.mb*dp.g/2)*(dp.Lss)^3/(48/64)*pi*A5)-dp.deltamax; %
ceq=[]; % There are no nonlinear equality constraints
end
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6 个评论
Works for me ?
Wheelchair()
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% Wheelchair
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function Wheelchair
% clc;
clear all; close all;
dp=design_parameters; % Generate structure variable containing all design parameters
f=@(x)objective_function(x,dp); % Objective function handle
nonlcon=@(x)nonlinear_constraints(x,dp); % Nonlinear constraint function handle
lb=[dp.X1_min;dp.X2_min;dp.X3_min;dp.X4_min];% Design variable bounds
ub=[dp.X1_max;dp.X2_max;dp.X3_max;dp.X4_max]; % Design variable bounds
x0=[25e-3;1.5e-3;20e-3;1e-3]; % Initial design point
A=[0,-(0.68*dp.db*dp.sigmau)/(0.3*dp.mb*dp.g),0,0;-1/dp.X1_min,0,0,0;1/dp.X1_max,0,0,0;0,-1/dp.X2_min,0,0;0,1/dp.X2_max,0,0;0,0,-1/dp.X3_min,0;0,0,1/dp.X3_max,0;0,0,0,-1/dp.X4_min;0,0,0,1/dp.X4_max];
b=[-1,-1,1,-1,1,-1,1,-1,1];
% Minimization using fmincon and the specified options:
options=optimoptions('fmincon','Display','iter','OptimalityTolerance',1e-12,'ConstraintTolerance',1e-6,'StepTolerance',1e-12);
[x,fval]=fmincon(f,x0,A,b,[],[],lb,ub,nonlcon,options);
% Display of the optimal design results:
disp(['Optimal value of d1: ',num2str(x(1)),' mm']);
disp(['Optimal value of t1: ',num2str(x(2)),' mm']);
disp(['Optimal value of d2: ',num2str(x(3)),' mm']);
disp(['Optimal value of t2: ',num2str(x(4)),'mm']);
disp(['Optimal value of f: ',num2str(fval),' kg']);
end
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function dp=design_parameters
dp.L1=0.82; %(m)
dp.L2=0.691; %(m)
dp.L3=0.270; %(m)
dp.L4=0.04; %(m)
dp.L5=1.59; %(m)
dp.L6=0.627; %(m)
dp.L7=0.568;
dp.Lad=0.601;
dp.Lss=0.4;
dp.mb=150;
dp.Rho=2700;
dp.g=10;
dp.db=6e-3;
dp.sigmau=110e6;
dp.E=70e9;
dp.K=2.1;
dp.deltamax=1e-3;
dp.X1_min=20e-3; dp.X1_max=30e-3; %
dp.X2_min=1e-3; dp.X2_max=2e-3; %
dp.X3_min=1e-3; dp.X3_max=22e-3; %
dp.X4_min=0.8e-3; dp.X4_max=1.5e-3;
end
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function f=objective_function(x,dp)
A1=(x(1)/2)^2-(x(1)/2-x(2))^2;
A2=(x(1)/2-x(2))^2-(x(1)/2-2*x(2))^2;
A3=((x(1)+2*x(2))^2-(x(1))^2)/4;
A4=(x(3)/2)^2-(x(3)/2-x(4))^2;
f=2*((pi*A1*dp.L1*dp.Rho)+(pi*A1*dp.L2*dp.Rho)+(pi*A2*dp.L3*dp.Rho)+(6*pi*A3*dp.L4*dp.Rho)+(0.097))+((pi*A4*dp.L5*dp.Rho)+(pi*A4*dp.L6*dp.Rho)+(pi*A4*dp.L7*dp.Rho)); %
end
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function [c,ceq]=nonlinear_constraints(x,dp)
A5=(x(1))^4-(x(1)-2*x(2))^4;
c(10,1)=0.2*dp.mb*dp.g-((pi.^3* dp.E/(dp.K*dp.Lad)^2*64)*A5); %
c(11,1)=((dp.mb*dp.g/2)*(dp.Lss)^3/(48/64)*pi*A5)-dp.deltamax; %
ceq=[]; % There are no nonlinear equality constraints
end
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Elmira Jafari
2021-12-15
Elmira Jafari
2021-12-15
编辑：Walter Roberson
2021-12-15
Walter Roberson
2021-12-15
That works for me as well, until the very end. You have
plot3(x(1),x(2),x(3),x(4),'b.','Markersize',20);
plot3() requires that the number of data arguments is a multiple of 3, whereas you have 4 data arguments.
If you had been using plot() instead of plot3() then you would have plotted two points.
I would suggest to you that plotting two points would be a bit difficult to understand unless perhaps you had a legend() that told you which point was which.
Torsten
2021-12-16
Note that the inequality constraints in the function "nonlinear_constraints" must be indexed starting with 1:
c(1)=0.2*dp.mb*dp.g-((pi.^3* dp.E/(dp.K*dp.Lad)^2*64)*A5); %
c(2)=((dp.mb*dp.g/2)*(dp.Lss)^3/(48/64)*pi*A5)-dp.deltamax; %
Walter Roberson
2021-12-16
While indexing from 1 is a good idea for clarity, it is not necessary. As usual when you first store into a matrix starting at an index other than 1, all elements from 1 to just before where you wrote are filled with 0. But for the purposes of constraints, 0 is fine: 0 satisfies the <= 0 test for nonlinear inequalities, and statisfies the == 0 for nonlinear equalities. So those entries c(1:9) being 0 are a small waste of time but do not affect convergence.
回答（2 个）
KSSV
2021-12-15
You may use scatter3
figure(1);
scatter3(x(1),x(2),x(3),[],x(4),'filled');
3 个评论
Elmira Jafari
2021-12-15
KSSV
2021-12-15
Then may you are expecting:
plot(x(1),x(2),'.b',x(3),x(4),'.r');
would you run this code?
wheel()
function wheel
clc; clear all; close all;
global ccc
rng('shuffle');
dp=design_parameters; % Generate structure variable containing all design parameters
f=@(x)objective_function(x,dp); % Objective function handle
g=@(x)nonlinear_constraints(x,dp); % Nonlinear constraint function handle
nvars=4; % Number of design variables
lb=[dp.X1_min;dp.X2_min;dp.X3_min;dp.X4_min];% Design variable bounds
ub=[dp.X1_max;dp.X2_max;dp.X3_max;dp.X4_max]; % Design variable bounds
A=[0,-(0.68*dp.db*dp.sigmau)/(0.3*dp.mb*dp.g),0,0;-1/dp.X1_min,0,0,0;1/dp.X1_max,0,0,0;0,-1/dp.X2_min,0,0;0,1/dp.X2_max,0,0;0,0,-1/dp.X3_min,0;0,0,1/dp.X3_max,0;0,0,0,-1/dp.X4_min;0,0,0,1/dp.X4_max];
b=[-1,-1,1,-1,1,-1,1,-1,1];
figure(1); hold on; grid on; box on; view(3);
axis([dp.X1_min dp.X1_max dp.X2_min dp.X2_max dp.X3_min dp.X3_max]);
xlabel('d1'); ylabel('t1'); zlabel('d2');
options=optimoptions('ga','PopulationSize',200,...
'EliteCount',5,...
'MaxGenerations',200,...
'MutationFcn',@mutationadaptfeasible,...
'CrossoverFcn',{@crossoverintermediate,1.5},...
'FunctionTolerance',1e-12,...
'ConstraintTolerance',1e-6,...
'HybridFcn','fmincon',...
'Display','iter',...
'PlotFcn',{@gaplotbestf,@gaplotrange},...
'OutputFcn',@custom_output);
% Call to the 'ga' function:
[x,fval,exitflag,output,population,scores]=ga(f,nvars,A,b,[],[],lb,ub,g,options);
disp(['Optimal value of d1: ',num2str(x(1)),' mm']);
disp(['Optimal value of t1: ',num2str(x(2)),' mm']);
disp(['Optimal value of d2: ',num2str(x(3)),' mm']);
disp(['Optimal value of t2: ',num2str(x(4)),'mm']);
disp(['Optimal value of f: ',num2str(fval),' kg']);
figure(1);
plot3(x(1),x(2),x(3),'b.','Markersize',20);
end
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function dp=design_parameters
dp.L1=0.82; %(m)
dp.L2=0.691; %(m)
dp.L3=0.270; %(m)
dp.L4=0.04; %(m)
dp.L5=1.59; %(m)
dp.L6=0.627; %(m)
dp.L7=0.568;
dp.Lad=0.601;
dp.Lss=0.4;
dp.mb=150;
dp.Rho=2700;
dp.g=10;
dp.db=6e-3;
dp.sigmau=110e6;
dp.E=70e9;
dp.K=2.1;
dp.deltamax=1e-3;
dp.X1_min=10e-3; dp.X1_max=20e-3; %
dp.X2_min=0.5e-3; dp.X2_max=1e-3; %
dp.X3_min=10e-3; dp.X3_max=20e-3; %
dp.X4_min=0.5e-3; dp.X4_max=1e-3;
end
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function f=objective_function(x,dp)
A1=(x(1)/2)^2-(x(1)/2-x(2))^2;
A2=(x(1)/2-x(2))^2-(x(1)/2-2*x(2))^2;
A3=((x(1)+2*x(2))^2-(x(1))^2)/4;
A4=(x(3)/2)^2-(x(3)/2-x(4))^2;
f=2*((pi*A1*dp.L1*dp.Rho)+(pi*A1*dp.L2*dp.Rho)+(pi*A2*dp.L3*dp.Rho)+(6*pi*A3*dp.L4*dp.Rho)+(0.097))+((pi*A4*dp.L5*dp.Rho)+(pi*A4*dp.L6*dp.Rho)+(pi*A4*dp.L7*dp.Rho)); %
end
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function [c,ceq]=nonlinear_constraints(x,dp)
A5=(x(1))^4-(x(1)-2*x(2))^4;
c(10,1)=0.2*dp.mb*dp.g-((pi.^3* dp.E/(dp.K*dp.Lad)^2*64)*A5); %
c(11,1)=((dp.mb*dp.g/2)*(dp.Lss)^3/(48/64)*pi*A5)-dp.deltamax; %
ceq=[]; % There are no nonlinear equality constraints
end
%%%%%%%%%%%%
function [state,options,optchanged]=custom_output(options,state,flag)
global ccc
title(['Current generation: ',num2str(state.Generation)]);
if ~isempty(ccc)
delete(ccc);
end
figure(1)
ccc=plot3(state.Population(:,1),state.Population(:,2),state.Population(:,3),'r.','Markersize',10);
pause(0.01);
optchanged = false;
end
Elmira Jafari
2021-12-15
编辑：Walter Roberson
2021-12-15
4 个评论
Walter Roberson
2021-12-15
You are not asking them to solve the same problem.
GA:
dp.E=70e9;
dp.X2_min=0.5e-3; dp.X2_max=1e-3; %
dp.X3_min=10e-3; dp.X3_max=16e-3; %
dp.X4_min=0.5e-3; dp.X4_max=1e-3;
fmincon:
dp.E=68e9;
dp.X2_min=0.5e-3; dp.X2_max=1.1e-3; %
dp.X3_min=10e-3; dp.X3_max=15e-3; %
dp.X4_min=0.5e-3; dp.X4_max=1.1e-3;
Walter Roberson
2021-12-15
Different constraints too.
GA:
c(10,1)=0.2*dp.mb*dp.g-((pi.^3* dp.E/(dp.K*dp.Lad)^2*64)*A5); %
% ^^^^
fmincon:
c(10,1)=0.2*dp.mb*dp.g*100-((pi.^3* dp.E/(dp.K*dp.Lad)^2*64)*A5); %
% ^^^^^^^^
Elmira Jafari
2021-12-15
Walter Roberson
2021-12-15
I broke all the functions out into separate files. I then edited wheel to call the functions you built for fmincon. It did not have any problem.
Single objective optimization:
4 Variable(s)
11 Nonlinear inequality constraint(s)
9 Linear inequality constraint(s)
Options:
CreationFcn: @gacreationlinearfeasible
CrossoverFcn: @crossoverintermediate
SelectionFcn: @selectionstochunif
MutationFcn: @mutationadaptfeasible
Best Max Stall
Generation Func-count f(x) Constraint Generations
1 10350 0.761147 0 0
2 20500 0.761047 0 0
3 38060 0.75681 0 0
4 64785 0.752895 0 0
5 95215 0.752255 0 0
6 131300 0.752225 0 0
Optimization terminated: average change in the fitness value less than options.FunctionTolerance
and constraint violation is less than options.ConstraintTolerance.
Switching to the hybrid optimization algorithm (FMINCON).
FMINCON terminated.
Optimal value of d1: 14 mm
Optimal value of t1: 1.0029 mm
Optimal value of d2: 10 mm
Optimal value of t2: 0.50061mm
Optimal value of f: 0.75222 kg
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