I write multi-objective differential evolution algorithm, using irank said dominant, namely (functions f1 and f2 values within the array value of other members have to laugh), idist said crowded distance (according to the ascending order of f1 function value, I instead of the nearest point of rectangular half the length), x and y represent two parameters, the two-dimensional space idea is initialized p1 - calculating idist and irank - mutation operation generates trials - p1 and trials to merge into the p2 - calculation of p2 irank and idist - select irank (priority) and idist big () at the same time when irank phase selection - first NP assigned to p1 and output of p1 fit1 and fit2 - computing idist and irank -- -- -- -- -- - loop calculation at present problem is 10 times with each output fit1 and fit2 constant

Non-professional students recently some times a little specification hope everybody to help!

 # include & lt; Iostream> 
#include  
#include  
#include  
#include  
using namespace std; 
# define NP 10 
2 * # define NP2 NP 
# define x_min 0.1 
# define x_max 1 
# define y_min 0 
# define y_max 5 


//generate a random number, random number is (0.0, 1.0) 
Double Rand_Double (void) 
{
Return static_cast & lt; Double> (rand ())/static_cast & lt; Double> (RAND_MAX); 
} 

//test functions f1 and f2 
//number of parameters to 2 
Double f1 (double x_1, double x_2) 
{
Return x_1; 
} 
Double f2 (double x_3, double x_4) 
{
The return (1 + x_4)/x_3; 

} 

//define individual classes, including feature values 
The class individual 
{
Public: 
Double x, y; 
Double fit1 fit2; 
Int irank; 
Double idist; 

Void copy (individual b) 
{
X=b.x; 
Y=b.y; 
Fit1=b. it1; 
Fit2=b. it2; 
Irank=b.i rank; 
Idist=b.i dist. 
} 


}; 

//definition exchange function 
Void swap (individual & amp; Eg_1, individual & amp; Eg_2) 
{
Individual temp_1; 
Temp_1. X=eg_1. X; 
Eg_1. X=eg_2. X; 
Eg_2. X=temp_1. X; 
Temp_1. Y=eg_1. Y; 
Eg_1. Y=eg_2. Y; 
Eg_2. Y=temp_1. Y; 
Temp_1. Fit1=eg_1. Fit1; 
Eg_1. Fit1=eg_2. Fit1; 
Eg_2. Fit1=temp_1. Fit1; 
Temp_1. Fit2=eg_1. Fit2; 
Eg_1. Fit2=eg_2. Fit2; 
Eg_2. Fit2=temp_1. Fit2; 
Temp_1. Irank=eg_1. Irank; 
Eg_1. Irank=eg_2. Irank; 
Eg_2. Irank=temp_1. Irank; 
Temp_1. Idist=eg_1. Idist; 
Eg_1. Idist=eg_2. Idist; 
Eg_2. Idist=temp_1. Idist; 
} 




//step 2 to calculate irank, adopt the mode of circular contrast statistics here number itself will not be dominant 
Void compute_irank (individual eg_3 [], int n) 
{

//for (int I=0; i//eg_3 [I] irank=0; 
for (int i=0; iFor (int j=I; J{
If (eg_3 [I] fit1 & lt;=eg_3 [j] fit1 & amp; & Eg_3 [I] fit2 & lt; Fit2) eg_3=eg_3 [j]. [I] irank=eg_3 [I] irank + 1; 
Else if (eg_3 [I] fit1 & gt;=eg_3 [j] fit1 & amp; & Eg_3 [I] fit2 & gt;=eg_3 [j] fit2) eg_3 [j]. J irank=eg_3 [j]. J irank + 1; 
} 
} 

//carried out in accordance with the formula one ascending order 
Void sort (individual eg_4 [], int n) 
{
Int I, j, p; 
For (I=0; i{
P=I; 
For (j=I + 1; JIf (eg_4 [j] fit1 & lt; Eg_4 [p]. Fit1) p=j; 
If (p!=I) 
Swap (eg_4 [p], eg_4 [I]); 
} 
} 

//screening of the next generation of population 
Void sort2 (individual eg_8 [], int n) 
{
Int I, j, p; 
For (I=0; i{
P=I; 
For (j=I + 1; J{
If (eg_8 [j] irank==eg_8 [p]. Irank) 
{
If (eg_8 [j] idist> Eg_8 [p]. Idist) p=j; 
} 
Else if (eg_8 [j]. Journal of irank> Eg_8 [p]. Irank) p=j; 
} 
If (p!=I) 
Swap (eg_8 [p], eg_8 [I]); 
} 
} 

//find idist constitute a rectangular length calculation two general, choose the nearest point and its adjacent points 
Void compute_idist (individual eg_5 [], int n) 
{
Double temp_2 temp_3; 
for (int i=0; i{
If (I==1) eg_5 [I] idist=SQRT ((eg_5 [I + 1] fit1 - eg_5 [I] fit1) * (eg_5 [I + 1] fit1 - eg_5 [I] fit1)) + SQRT ((eg_5 [I + 1] fit2 - eg_5 [I] fit2) * (eg_5 [I + 1] fit2 - eg_5 [I] fit2)); 
Else if (I==n - 1) eg_5 [n - 1]. Idist=SQRT ((eg_5 [I + 1] fit1 - eg_5 [I] fit1) * (eg_5 [I + 1] fit1 - eg_5 [I] fit1)) + SQRT ((eg_5 [I + 1] fit2 - eg_5 [I + 1] fit2) * (eg_5 [I + 1] fit2 - eg_5 [I + 1] fit2)); 
The else 
{
Temp_2=SQRT ((eg_5 [I] fit1 - eg_5 [I - 1]. Fit1) * (eg_5 [I] fit1 - eg_5 [I - 1]. Fit1) + (eg_5 [I] fit2 - eg_5 [I - 1]. Fit2) * (eg_5 [I] fit2 - eg_5 [I - 1]. Fit2)); 
Temp_3=SQRT ((eg_5 [I + 1] fit1 - eg_5 [I] fit1) * (eg_5 [I + 1] fit1 - eg_5 [I] fit1) + (eg_5 [I + 1] fit2 - eg_5 [I + 1] fit2) * (eg_5 [I + 1] fit2 - eg_5 [I + 1] fit2)); 
If (temp_2 & gt;=temp_3) eg_5 [I] idist=SQRT ((eg_5 [I + 1] fit1 - eg_5 [I] fit1) * (eg_5 [I + 1] fit1 - eg_5 [I] fit1)) + SQRT ((eg_5 [I + 1] fit2 - eg_5 [I] fit2) * (eg_5 [I + 1] fit2 - eg_5 [I] fit2)); 
The else eg_5 [I] idist=SQRT ((eg_5 [I] fit1 - eg_5 [I - 1]. Fit1) * (eg_5 [I] fit1 - eg_5 [I - 1]. Fit1)) + SQRT ((eg_5 [I] fit2 - eg_5 [I - 1]. Fit2) * (eg_5 [I] fit2 - eg_5 [I - 1]. Fit2)); 
} 
} 
} 





Void main () 

{
//-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- set of random Numbers -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 
Srand ((unsigned int) (time (NULL))); 

//get parameter 
Int Gen=10; 
Double F=0.5, cr=0.5; 
int i; 

P1, individual * * trials, * (P2); 
P1=new individual (NP), 
Trials=new individual (NP), 
The P2=new individual [NP2]; 

//-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- the initial random population -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 
For (I=0; i{

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