I don't understand why does it give error?

Question

Sadiq Akbar 2023-1-5

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1888712-i-don-t-understand-why-does-it-give-error

评论： Alan Stevens 2023-1-8

I downloaded the following code from Mathworks site whose URL is :

URL: https://www.mathworks.com/matlabcentral/fileexchange/52949-the-butterfly-particle-swarm-optimization-butterfly-pso-bf-pso-technique?s_tid=FX_rc1_behav

But when I run it, it gives an error. The code is:

%######### Example_Functions.m file ###########
%#### Write the several benchmark functions for BF-PSO here example of sphere is given ###
function fitns_fn=Fitness_fn(x) % Sphere function
fitns_fn=sum(x.^2);
end
%########### Function_limit.m file #######
%#### Give the limits of benchmark functions for BF-PSO ###
function[minm,maxm]=Function_limit(Example_Function) 
    minm=-100; % minimum value of dmension (or variables)
    maxm=100; % maximum value of dmension (or variables)
end
%########### BF-PSO.m file #############
%####### Butterfly-PSO (or BF-PSO)#########
clear all
clc
format short
global bf
global dm
global Example_Function
[minm,maxm]=Function_limit(Example_Function);
ft_fn='Fitness_fn';
%############### Initialization ##############
 flight_max  = 1000; % Max number of flights by BF(or iterations) 
itera_max=flight_max;
bf = 20;    % Size of the butterfly swarm
dm = 30;    % dimensions, 2, 10, 20, 30 ( or problem variables)
c1 =2;  % Acceleration rate C1 
c2 =2;  % Acceleration rate C2 
pg=1;
locations = rand(dm, bf).*(maxm-minm)+minm;
%%%% velocity limits as per variable problems;%%%%%% 
vmax=((maxm-minm)/(rand*maxm));
vmin=-vmax;
velocity = rand(dm, bf).*(vmax-vmin)+vmin;
current_fitness = feval(ft_fn,locations);
local_best_locations  = locations;
mean_best_locations=mean(local_best_locations,2);
local_best_fitness  = current_fitness;
[global_best_fitness,Ind] = min(local_best_fitness);
globl_best_locations = local_best_locations(:,Ind);
     %%%%%%%%%%% Main iterations counter Loop %%%%%%%%%
itera=0;
while (itera < itera_max) % start main iteration loop
      itera = itera+1; p=ones(dm,bf)*(global_best_fitness./(sum(local_best_fitness)));  %%% probability of nectar
        ptt=(1-pt);
s=ones(dm, bf)*exp(-(itera_max-itera)/itera_max); % sensitivity of butterflies
w=(itera_max-itera)/itera_max;
velocity=w.*velocity+ptt.*s.*c1.*rand(dm,bf).*(local_best_locations-locations)+...
pg.*c2.*rand(dm,bf).*((globl_best_locations-locations)*ones(1,bf));   % velocity updation
velocity=min(vmax,max(vmin,velocity));% check velocity limits
locations=locations+rand.*pt.*velocity); 
% location updation
locations=min(maxm, max(minm,locations)); % check location or variable limits
%%%%%%%%%%%%%%%%%% Updating local, mean and global best %%%%%%% 
current_fitness=feval(ft_fn,locations);
Ifit=find(current_fitness<local_best_fitness);
local_best_fitness(:,Ifit)=current_fitness(:,Ifit);
local_best_locations(:,Ifit)=locations(:,Ifit);
 [current_global_best_fitness,Ind]=min(local_best_fitness);
    if current_global_best_fitness < global_best_fitness
       global_best_fitness = current_global_best_fitness;
    end 
globl_best_locations=local_best_locations(:,Ind);
minfit2(itera+1)=global_best_fitness; 
% record min fitness value for plot   
end % end of main iteration loop
min_fit=global_best_fitness     
globl_best_locati=globl_best_locations
bf_mean_fit=mean(minfit2)
bf_stdvn_fit=std(minfit2)
%%%%%% plots %%%%%%%
itera1=0:length(minfit2)-1;
plot(itera1, minfit2,'b')
xlabel('Iterations');
ylabel('Fitness Value');
legend('BF-PSO')

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Alan Stevens 2023-1-5

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1888712-i-don-t-understand-why-does-it-give-error#answer_1141887

在 MATLAB Online 中打开

Put the functions at the end. Tidy up one or two minor errors to get:

%########### BF-PSO.m file #############

%####### Butterfly-PSO (or BF-PSO)#########

clear all

clc

format short

global bf

global dm

global Example_Function

[minm,maxm]=Function_limit(Example_Function);

ft_fn='Fitness_fn';

%############### Initialization ##############

flight_max = 1000; % Max number of flights by BF(or iterations)

itera_max=flight_max;

bf = 20; % Size of the butterfly swarm

dm = 30; % dimensions, 2, 10, 20, 30 ( or problem variables)

c1 =2; % Acceleration rate C1

c2 =2; % Acceleration rate C2

pg=1;

locations = rand(dm, bf).*(maxm-minm)+minm;

%%%% velocity limits as per variable problems;%%%%%%

vmax=((maxm-minm)/(rand*maxm));

vmin=-vmax;

velocity = rand(dm, bf).*(vmax-vmin)+vmin;

current_fitness = feval(ft_fn,locations);

local_best_locations = locations;

mean_best_locations=mean(local_best_locations,2);

local_best_fitness = current_fitness;

[global_best_fitness,Ind] = min(local_best_fitness);

globl_best_locations = local_best_locations(:,Ind);

%%%%%%%%%%% Main iterations counter Loop %%%%%%%%%

itera=0;

% You need to define pt (arbitrary value here)

pt = 0.1;

while (itera < itera_max) % start main iteration loop

itera = itera+1; p=ones(dm,bf)*(global_best_fitness./(sum(local_best_fitness))); %%% probability of nectar

ptt=(1-pt);

s=ones(dm, bf)*exp(-(itera_max-itera)/itera_max); % sensitivity of butterflies

w=(itera_max-itera)/itera_max;

velocity=w.*velocity+ptt.*s.*c1.*rand(dm,bf).*(local_best_locations-locations)+...

pg.*c2.*rand(dm,bf).*((globl_best_locations-locations).*ones(1,bf)); % velocity updation

velocity=min(vmax,max(vmin,velocity));% check velocity limits

locations=locations+rand.*pt.*velocity;

% location updation

locations=min(maxm, max(minm,locations)); % check location or variable limits

%%%%%%%%%%%%%%%%%% Updating local, mean and global best %%%%%%%

current_fitness=feval(ft_fn,locations);

Ifit=find(current_fitness<local_best_fitness);

local_best_fitness(:,Ifit)=current_fitness(:,Ifit);

local_best_locations(:,Ifit)=locations(:,Ifit);

[current_global_best_fitness,Ind]=min(local_best_fitness);

if current_global_best_fitness < global_best_fitness

global_best_fitness = current_global_best_fitness;

end

globl_best_locations=local_best_locations(:,Ind);

minfit2(itera+1)=global_best_fitness;

% record min fitness value for plot

end % end of main iteration loop

min_fit=global_best_fitness;

globl_best_locati=globl_best_locations;

bf_mean_fit=mean(minfit2);

bf_stdvn_fit=std(minfit2);

%%%%%% plots %%%%%%%

itera1=0:length(minfit2)-1;

plot(itera1, minfit2,'b')

xlabel('Iterations');

ylabel('Fitness Value');

legend('BF-PSO')

%######### Example_Functions.m file ###########

%#### Write the several benchmark functions for BF-PSO here example of sphere is given ###

function fitns_fn=Fitness_fn(x) % Sphere function

fitns_fn=sum(x.^2);

end

%########### Function_limit.m file #######

%#### Give the limits of benchmark functions for BF-PSO ###

function[minm,maxm]=Function_limit(Example_Function)

minm=-100; % minimum value of dmension (or variables)

maxm=100; % maximum value of dmension (or variables)

end

5 个评论
显示 3更早的评论隐藏 3更早的评论

Sadiq Akbar 2023-1-6

在 MATLAB Online 中打开

Thank you dear Alan Stevens for your help. Yest it works now. But when I checked it with the following function listed at the very bottom under listed my function , then it doesn't work:

%########### BF-PSO.m file #############
%####### Butterfly-PSO (or BF-PSO)#########
clear all
clc
format short
global bf
global dm
global Example_Function
u=[1 2 30 40];
[minm,maxm]=Function_limit(Example_Function);
ft_fn=@(b)karimVectorized1(b,u);%'Fitness_fn';
%############### Initialization ##############
 flight_max  = 1000; % Max number of flights by BF(or iterations) 
itera_max=flight_max;
bf = 20;    % Size of the butterfly swarm
dm = 4;%30;    % dimensions, 2, 10, 20, 30 ( or problem variables)
c1 =2;  % Acceleration rate C1 
c2 =2;  % Acceleration rate C2 
pg=1;
locations = rand(dm, bf).*(maxm-minm)+minm;
%%%% velocity limits as per variable problems;%%%%%% 
vmax=((maxm-minm)/(rand*maxm));
vmin=-vmax;
velocity = rand(dm, bf).*(vmax-vmin)+vmin;
current_fitness = feval(ft_fn,locations);
local_best_locations  = locations;
mean_best_locations=mean(local_best_locations,2);
local_best_fitness  = current_fitness;
[global_best_fitness,Ind] = min(local_best_fitness);
globl_best_locations = local_best_locations(:,Ind);
     %%%%%%%%%%% Main iterations counter Loop %%%%%%%%%
itera=0;
% You need to define pt (arbitrary value here)
pt = 0.1;
while (itera < itera_max) % start main iteration loop
      itera = itera+1; p=ones(dm,bf)*(global_best_fitness./(sum(local_best_fitness)));  %%% probability of nectar
        ptt=(1-pt);
s=ones(dm, bf)*exp(-(itera_max-itera)/itera_max); % sensitivity of butterflies
w=(itera_max-itera)/itera_max;
velocity=w.*velocity+ptt.*s.*c1.*rand(dm,bf).*(local_best_locations-locations)+...
pg.*c2.*rand(dm,bf).*((globl_best_locations-locations).*ones(1,bf));   % velocity updation
velocity=min(vmax,max(vmin,velocity));% check velocity limits
locations=locations+rand.*pt.*velocity; 
% location updation
locations=min(maxm, max(minm,locations)); % check location or variable limits
%%%%%%%%%%%%%%%%%% Updating local, mean and global best %%%%%%% 
current_fitness=feval(ft_fn,locations);
Ifit=find(current_fitness<local_best_fitness);
local_best_fitness(:,Ifit)=current_fitness(:,Ifit);
local_best_locations(:,Ifit)=locations(:,Ifit);
 [current_global_best_fitness,Ind]=min(local_best_fitness);
    if current_global_best_fitness < global_best_fitness
       global_best_fitness = current_global_best_fitness;
    end 
globl_best_locations=local_best_locations(:,Ind);
minfit2(itera+1)=global_best_fitness; 
% record min fitness value for plot   
end % end of main iteration loop
min_fit=global_best_fitness;     
globl_best_locati=globl_best_locations;
bf_mean_fit=mean(minfit2);
bf_stdvn_fit=std(minfit2);
%%%%%% plots %%%%%%%
itera1=0:length(minfit2)-1;
plot(itera1, minfit2,'b')
xlabel('Iterations');
ylabel('Fitness Value');
legend('BF-PSO')
%######### Example_Functions.m file ###########
%#### Write the several benchmark functions for BF-PSO here example of sphere is given ###
function fitns_fn=Fitness_fn(x) % Sphere function
fitns_fn=sum(x.^2);
end
%########### Function_limit.m file #######
%#### Give the limits of benchmark functions for BF-PSO ###
function[minm,maxm]=Function_limit(Example_Function) 
    minm=-100; % minimum value of dmension (or variables)
    maxm=100; % maximum value of dmension (or variables)
end
%%%%%%%%%%% My Function %%%%%%%%%%%%%%%
function e=karimVectorized1(b,u)
u=u';
b=b';
[R,~] = size(b);
P = R/2;
M = 2*R;
%%%%%%%%%%%%%%%%%%%%%%%%
% Swapping vector b 
%%%%%%%%%%%%%%%%%%%%%%%%
[~, ix] = sort(u); % u is my desired vector
[~, ix1(ix)] = sort(b); 
b = b(ix1);
%%%%%%%%%%%%%%%
% calculate xo
%%%%%%%%%%%%%%%
u1 = u(    1:P );
u2 = u( P+(1:P));
k = 1:M;
xo = sum( u1.*exp( -1i.*pi*cosd(u2).*(k-1) ) , 1);
% xo = awgn(xo,Noise);% add Noise
%%%%%%%%%%%%%%%
% Calculate xe
%%%%%%%%%%%%%%%
b1 = b(    1:P );
b2 = b( P+(1:P));
xe = sum( b1.*exp( -1i.*pi*cosd(b2).*(k-1) ) , 1);
%%%%%%%%%%%%
% MSE
%%%%%%%%%%%%
%e = mean( abs( xo(1,:) - xe(1,:) ) .^2 );% Both are correct
e = mean(abs(xo-xe).^2,2);
end

It gives the following error:

Unable to perform assignment because the indices on the left side are not compatible with

the size of the right side.

Error in BFPSO_CorrectedByMathworks>karimVectorized1 (line 97)

[~, ix1(ix)] = sort(b);

Error in BFPSO_CorrectedByMathworks>@(b)karimVectorized1(b,u) (line 12)

ft_fn=@(b)karimVectorized1(b,u);%'Fitness_fn';

Error in BFPSO_CorrectedByMathworks (line 26)

current_fitness = feval(ft_fn,locations);

Sadiq Akbar 2023-1-8

@Alan Steven

Thank you very much dear Alan Stevens for your kind response. I am giving both the codes in the attachment. One is that corrected by you called "BFPSO_AlanStevens.m" and one is that changed by me called "BFPSO_mine.m". As you can see "BFPSO_mine" is also the same as is yours. But this shows the changes where I have made in yours. It works from the start lines but gives the error where it calls my function. And my function is given at the very bottom of the whole code. You can locate it very easily now. But why it is giving an error, that I don't undesratnd. Further, it also includes the value of Pt you defined. But the reason of error is not understood by me. The only difference in the two functions is that my function has 4 variables and has lower bound as [0 0 0 0] and upper bound as [10 10 90 90] but the original function has 30 variables and all have lower bound as -100 and upper bound as 100.

Regards,

Alan Stevens 2023-1-8

在 MATLAB Online 中打开

In your function b1 and b2 have different sizes from k, hence you can't do element by element multiplication as required in xe. I don't know enough about exactly what your program is modelling to help any further,

%%%%%%%%%%% My Function %%%%%%%%%%%%%%% 
function e=karimVectorized1(b,u) %My function------------(8)
u=u';
b=b';
[R,~] = size(b);
P = R/2;
M = 2*R;
%%%%%%%%%%%%%%%%%%%%%%%%
% Swapping vector b 
%%%%%%%%%%%%%%%%%%%%%%%%
[~, ix] = sort(u); % u is my desired vector
[~, ix1(ix,:)] = sort(b); %%%%%%%%%%%%%%%%%%%%%%%%%%%
b = b(ix1);
%%%%%%%%%%%%%%%
% calculate xo
%%%%%%%%%%%%%%%
u1 = u(    1:P );
u2 = u( P+(1:P));
k = 1:M;
xo = sum( u1.*exp( -1i.*pi*cosd(u2).*(k-1) ) , 1);
% xo = awgn(xo,Noise);% add Noise
%%%%%%%%%%%%%%%
% Calculate xe
%%%%%%%%%%%%%%%
b1 = b(    1:P );
b2 = b( P+(1:P));
% b1 and b2 have size 1x2, but k has size 1x8 hence the bracketed term 
% in the following line cannot be executed.  
xe = sum( b1.*exp( -1i.*pi*cosd(b2).*(k-1) ) , 1);
%%%%%%%%%%%%
% MSE
%%%%%%%%%%%%
%e = mean( abs( xo(1,:) - xe(1,:) ) .^2 );% Both are correct
e = mean(abs(xo-xe).^2,2);
end

请先登录，再进行评论。

I don't understand why does it give error?

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

回答（1 个）

5 个评论
显示 3更早的评论隐藏 3更早的评论

另请参阅

类别

标签

Community Treasure Hunt

I don't understand why does it give error?

0 个评论 显示 -2更早的评论隐藏 -2更早的评论

回答（1 个）

5 个评论 显示 3更早的评论隐藏 3更早的评论

另请参阅

类别

标签

Community Treasure Hunt

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

5 个评论
显示 3更早的评论隐藏 3更早的评论