Optimization Problem cost function won't minimize after generations

Question

Christopher Jarrett 2021-3-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/785631-optimization-problem-cost-function-won-t-minimize-after-generations

Optimization.png

Hi I have a matlab project where the best cost value should be getting smaller after each generation but Its not. Ive tried to do alot of debugging but nothing has worked. The plot just hoover's around the same value, meaning its not optimizing.

clear; clc; close all;
%% Creating Initial Random population 
R = 20000 + (50000-20000)*rand(1);
C = 0.0001 +(0.001-0.0001)*rand(1);
Vs = 5 +(20-5)*rand(1);
tc = 1+(8-2)*rand(1);
RandPopulation = zeros(100,4);
%% Step 2 Calculate cost function
    for i = 1:100
    R1 = 20000 + (50000-20000)*rand(1);
    C1 = 0.0001 +(0.001-0.0001)*rand(1);
    Vs1 = 5 +(20-5)*rand(1);
    tc1 = 1+(8-2)*rand(1);
    RandPopulation( i,1) = R1; 
    RandPopulation( i,2) = C1; 
    RandPopulation( i,3) = Vs1; 
    RandPopulation( i,4) = tc1;
   
    end
    dt=0.01; 
    t=0:dt:10;
    n=size(t);
    A= zeros(100,1); %cost function A
    for j= 1:100
    
   R = RandPopulation( j,1);  
   C = RandPopulation( j,2); 
   Vs = RandPopulation( j,3); 
   tc = RandPopulation( j,4);
   
    Vc=zeros(1,n(2));
    Vc(1)=0;
    
 
    
%% EULER SOLUTION
% Charge
    limit = round(tc/dt);
    for i=2:limit+1
    
        Vc(i)=Vc(i-1)+(Vs/(R*C)-Vc(i-1)/(R*C))*dt;
    end
    %Discharge 
    for i=(limit+2):n(2)
        Vc(i)=Vc(i-1)+(-Vc(i-1)/(R*C))*dt;
    end
    
    A(j) = ( Vc(1)-0)^2 + (Vc(200)-3.27)^2 + (Vc(400)-5.79)^2 + (Vc(600)-7.70)^2 + (Vc(800)-6.64)^2 + (Vc(1000)-5.09)^2 ;
    end 
        B= A(1:100);
         RandPopulation = [RandPopulation B];
               
%% Execute a for loop that will plot the top 10 cost function values, 
%% for each generation vs each generation of matrix
%% for 100 generations  
for k= 1:100
%% Step 3 Sort Your Matrix
SortPopulation  = sortrows(RandPopulation,5);
%% Creating 2nd generation of Empty Population
NewPopulation = zeros(100,5);
%% Step 4 Create 1st generation of 20 Children from top 40 performers
ChildPopulation = zeros(20,5);
j=1;
for i = 1:1:20
    
  phi= rand(1);  
  R1 = SortPopulation(i,1);
  R2 = SortPopulation(i+1,1);
  AvgR = (phi*R1) + (R2*(1-phi));
  
  phi = rand(1);
  C1 = SortPopulation(i,2);
  C2 = SortPopulation(i+1,2);
  AvgC = (phi*C1) + (C2*(1-phi));
  
  phi = rand(1);
  Vs1 = SortPopulation(i,3);
  Vs2 = SortPopulation(i+1,3);
  AvgVs = (phi*Vs1) + (Vs2*(1-phi));
  
  phi = rand(1);
  tc1 = SortPopulation(i,4);
  tc2 = SortPopulation(i+1,4);
  Avgtc = (phi*tc1) + (tc2*(1-phi));
  
  phi = rand(1);
  B1= SortPopulation(i,5);
  B2= SortPopulation(i+1,5);
  AvgB = (phi*B1) + (B2*(1-phi));
  
  ChildPopulation(j,1)= AvgR;
  ChildPopulation(j,2)= AvgC;
  ChildPopulation(j,3)= AvgVs;
  ChildPopulation(j,4)= Avgtc;
  ChildPopulation(j,5)= AvgB;
  
  j=j+1;
end
%% Step 5 Creating 60 New Strings
NewRandPopulation = zeros(60,4);
for i = 1:60
R1 = 20000 + (50000-20000)*rand(1);
C1 = 0.0001 +(0.001-0.0001)*rand(1);
Vs1 = 5 +(20-5)*rand(1);
tc1 = 1+(8-2)*rand(1);
   NewRandPopulation( i,1) = R1; 
   NewRandPopulation( i,2) = C1; 
   NewRandPopulation( i,3) = Vs1; 
   NewRandPopulation( i,4) = tc1;
   
end
   
dt=0.01; 
t=0:dt:10;
n=size(t);
A2= zeros(60,1); %cost function2
for j= 1:60
    
   R = NewRandPopulation( j,1);  
   C = NewRandPopulation( j,2); 
   Vs = NewRandPopulation( j,3); 
   tc = NewRandPopulation( j,4);
   
    Vc=zeros(1,n(2));
    Vc(1)=0;
    
%% EULER SOLUTION2
% Charge
    limit = round(tc/dt);
    for i=2:limit+1
    
        Vc(i)=Vc(i-1)+(Vs/(R*C)-Vc(i-1)/(R*C))*dt;
    end
    %Discharge 
    for i=(limit+2):n(2)
        Vc(i)=Vc(i-1)+(-Vc(i-1)/(R*C))*dt;
    end
%% COST FUNCTION2
%func
    A2(j) = ( Vc(1)-0)^2 + (Vc(200)-3.27)^2 + (Vc(400)-5.79)^2 + (Vc(600)-7.70)^2 + (Vc(800)-6.64)^2 + (Vc(1000)-5.09)^2 ;
end 
B2= A2(1:60);
NewRandPopulation = [NewRandPopulation B2];
%% Step 6 combine the matrices keep the top 20 Parents  
NewPopulation(21:40,:)  =  ChildPopulation;
NewPopulation(41:100,:) =  NewRandPopulation;
NewPopulation(1:20,:)   =  SortPopulation(1:20,:);
SortNewPopulation  = sortrows(NewPopulation,5);
%% Overwriting the old population with the new population (possibly needing consideration of for loop)
BestCost(k) = SortNewPopulation(1,5);
gen(k)=k+1;
end
y= BestCost
x = gen
plot(gen,BestCost)
ylabel('Best cost function')
xlabel('generations')
%%NewPopulation = RandPopulation;
%end
%plot(Gens,BestCost);
%xlabel('generations')
%ylabel('Best cost function values per generation')