What mean by this fitness coding? I'm not understand. What is "ones" means?

This is the coding

clear
clc
n = 50;          % Size of the swarm " no of birds "
bird_step  = 50; % Maximum number of "birds steps"
dim = 10;          % Dimension of the problem

c2 =1.2;          % PSO parameter C1 
c1 = 0.12;        % PSO parameter C2 
w =0.9;           % pso momentum or inertia  
fitness=0*ones(n,bird_step);    %fitness population


%**********************************%
%  Initialize the parameter
%**********************************%
R1 = rand(dim, n);
R2 = rand(dim, n);
current_fitness =0*ones(1,n);

%****************************************************%
%  Initializing swarm and velocities and position
%****************************************************%
%Initialize random number of generator
current_position = 10*(rand(dim, n)-.5);
velocity = .3*randn(dim, n) ;
local_best_position  = current_position ;

%**********************************%
%  Evaluate initial population
%**********************************%
for i = 1:n
    current_fitness(i) = rosenbrock(current_position(:,i));    
end


local_best_fitness  = current_fitness ;
[global_best_fitness,g] = min(local_best_fitness) ;

for i=1:n
    globl_best_position(:,i) = local_best_position(:,g) ;
end

%**********************************%
%  Optimization
%**********************************%

%------VELOCITY UPDATE------%
velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));


%------SWARMUPDATE------%
current_position = current_position + velocity ;

%------EVALUATE NEW SWARM------%
global_best_fitness = inf;

%% MAIN LOOP %%
iter = 0 ;        % Iterationscounter
while  ( iter < bird_step )
iter = iter + 1;

for i = 1:n,
current_fitness(i) = rosenbrock(current_position(:,i)) ;    

end


for i = 1 : n
        if current_fitness(i) < local_best_fitness(i)
           local_best_fitness(i)  = current_fitness(i);  
           local_best_position(:,i) = current_position(:,i)   ;
        end   
 end

  
 [current_global_best_fitness,g] = min(local_best_fitness);
  
    
if current_global_best_fitness < global_best_fitness
   global_best_fitness = current_global_best_fitness;
   
    for i=1:n
        globl_best_position(:,i) = local_best_position(:,g);
    end
   
end


 velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));
 current_position = current_position + velocity; 
  
 

 
x=current_position(1,:);
y=current_position(2,:);

clf    
    plot(x, y , 'h')   
    axis([-5 5 -5 5]);
    
pause(.3)


end % end of while loop its mean the end of all step that the birds move it 
                      

              [Jbest_min,I] = min(current_fitness) % minimum fitness
               current_position(:,I) % best solution

               


    

% 

What i dont understand is...

fitness=0*ones(n,bird_step);    %fitness population

What means by zero multiply by ones?

below is the rosenbrock.m

function f=ui(X)
     %fitness function
n=length(X);
f=0;
for i=1:n-1
    f=f+100*(X(i,1)*X(i,1)-X(i+1,1))^2+(X(i,1)-1)^2;%summation function f = f + ...
end

[Walter Roberson]

it would have been clearer if the user had coded

fitness=zeros(n,bird_step);    %fitness population

There are some very subtle efficiency differences in using 0*ones(), and some differences in internal preallocation that make no difference unless you are using an array larger than half of your RAM.

Mostly, some people prefer 0*ones() by analogy to something like 3.7*ones() which would be a strategy to set all the entries to 3.7. They feel that it is inconsistent to write zeros() for the case of setting to 0 while having to use Constant*ones() for setting the value to Constant.