Solving Optimization Problems

Adaptive Restart Hybrid Genetic Algorithm

function Y=population(p,d,LR,UR)
% d = dimensions of the problem
% p = population size
% LR = Lower bound
% UR = Up bound
Y=(UR-LR)*rand(p,d)+LR;

function Y=crossover(X,n)
% X = population
% n = no of pairs of chromosomes crossover
[x y]=size(X);
E=zeros(2*n,y);
for i= 1:n
    r=randi(x,1,2);
    while r(1)==r(2)
        r=randi(x,1,2);
    end
    % Note: r = selec two chromsomes for crossover
    A=X(r(1),:); % chromosome 1 for crossover
    B=X(r(2),:); % chromosome 2 for crossover
    c=1+randi(y-1); % select cut point
    D=A(1,c:y); % reserve
    A(1,c:y)=B(1,c:y);
    B(1,c:y)=D;
    % NOTE: A and B are chromosomes after crossover
    E(2*i-1,:)=A;
    E(2*i,:)=B;
end
Y=E;

function Y=mutation(X,n)
% X = population
% n = no of pairs of chromosomes crossover
[x y]=size(X);
E=zeros(2*n,y);
for i= 1:n
    r=randi(x,1,2);
    while r(1)==r(2)
        r=randi(x,1,2);
    end
    % Note: r = selec two chromsomes for mutation
    A=X(r(1),:); % chromosome 1 for mutation
    B=X(r(2),:); % chromosome 2 for mutation
    
    c=randi(y); % select cut point
    D=A(1,c); % reserve
    A(1,c)=B(1,c);
    B(1,c)=D;
    % NOTE: A and B are chromosomes after crossover
    E(2*i-1,:)=A;
    E(2*i,:)=B;
end
Y=E;

function YY=local_search(X,n,s,LR,UR)
% X = current best solution
% n = no. of chromosomes seleected
%clc
%clear all
%close all
%X=population(1);
%=====================================================
[x y]=size(X);
A=ones(2*y,1)*X;
j=1;

for i=1:y
...
end
% NOTE: A = LOCAL SOLUTION 
%=======================================================
% Randomly select n local solutions
[x1 y1]=size(A);
Y=zeros(n,y1);
for k = 1:n
...
end
[x2 y2]=size(Y);
for i=1:x2
    for j=1:y2
...
end
YY=Y;

function Y = evaluation(X)
% X = population
[x y]=size(X);
d=y; % number of dimensions
C=zeros(1,x);
for j=1:x
    P=X(j,:);
    B=zeros(2,d);
    for i=1:d
        x1=P(1,i);
        B(1,i)=x1^2;
        B(2,i)=cos(2*pi*x1);
    end
    Z=-20*exp(-0.2*sqrt(sum(B(1,:))/d))-exp(sum(B(2,:))/d)+20+exp(1);
    C(1,j)=1/Z; % obbjective function
end
Y=C;

function [YY1 YY2] = selection(P1,B,p,s)
% P1 - population
% B - fitness value 
% p - population size
% s = select top s chromsomes
%-------------------------------------------------------------------------
% Top selection operation
for i =1:s
    [r1 c1]=find(B==max(B));
    Y1(i,:)=P1(max(c1),:);
    Fn(i)=1/B(max(c1));
    P1(max(c1),:)=[];
    B(:,max(c1))=[];
    clear r1 c1
end
% Determine total fitness for the population
C=sum(B);
% Determine selection probability
D=B/C;
% Determine cumulative probability 
E= cumsum(D);
% Generate a vector constaining normalised random numbers
N=rand(1);
d1=1;
d2=s;
while d2 <= p-1
...
...
end
YY1=Y1;
YY2=Fn;
end

clear all
clc
close all
%--------------------------------------------------------------------------
dim = 10; % dimensions of the problem
LR=-15; % Lower bound
UR=30;  % Up bound
%----------------------------------------------------------------
p= 100; % population size
c= 2; % crossover rate
m= 10; % mutation rate
s= 50; % number of generations to restart if getting stuck in local minimum
g= 1; % elite chromosomes
r= 1;% number of initial chromosomes
n= 4; % no of chromosomes RAMDOMLY selected from local (in %)
tg=500; % Total generations
redu=0.1;
%--------------------------------------------------------------------------
P1=population(r,dim,LR,UR); % Initial guess
w=1;
j=1;
K=0;
KK=0;
ww=1;
figure
title('Blue - Minimum         Red - Average');
xlabel('Generation')
ylabel('Objective function value')
hold on
while j <=tg
        P=population(p-r,dim,LR,UR);
        P(p-r+1:p,:)=P1;
        % Extended population
        lp=1;
        t= 15;% initial step size
        while lp==1
...
        end
end
minimum=min(K(:,2))
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