Solving a Travelling Salesman Problem (TSP) Using Adaptive Restart Genetic Algorithm

function Y=population(n)
% 10 cities (1-10)

B=zeros(n,10);
for j=1:n
    A=1:1:10;   % list of 10 cities
    [x y]=size(A);
    for i = 1:10
        r = randi(y);
        B(j,i)=A(r);
        A(:,r) = [];
        [x y]=size(A);
    end
end
Y=B;

function YY = crossover(X,n)
% X = population
% n = number of chromosomes to be crossed
%..........................................................................
[x1 y1] = size(X);
Y = zeros(n,y1);

for z = 1:n
    B = X(randi(x1),:); % select parent chromosome
    r1 = 1 + randi(y1-1);
.
.
.

function Y=mutation(X,n)
% X = population
% n = number of chromosomes to be mutated
%..........................................................................
[x1 y1]=size(X);
Y=zeros(n,y1);
for z=1:n
    A=X(randi(x1),:); % select parent chromosome
    r1=1+randi(y1-1,1,2);
    while r1(1)==r1(2)
            r1=1+randi(y1-1,1,2);
.
.
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function Y = evaluation(P)
% P = population

% Distances between 10 cities
%  city 1     2     3    4      5     6     7    8      9       10
    D=[ 0   2.7 4.3  1.3 2.4   4.8 4.9  19     22.3 34.4; %city 1
        2.7   0     1.9  4     0.9   2.1 3.6  17.5 21     32.9; %city 2
        6.3   1.6 0  6.3 2.2   1.7 2.3  19.8 19.7 35.2; %city 3
        1.7   4.5 6.1  0     4.1   5.1 6.5  18.2 24.1 33.5; %city 4
        2.5   0.9 2.1  4.1 0   3.7 2.7  18.3 20.1 33.6; %city 5
        4.8   1.8 1.7  4.8 2.3   0     3.7  18.3 21.1 33.7; %city 6
        5   3.4 2.2  6.5 3   3.7 0  20.7 17.8 36.1; %city 7
        19.1  17.8 19.8 18.1 18.1  18.3 20.7 0     38.1 21.3; %city 8
        22.4  20.8 19.5 24.2 20.4  21 17.8 38.2 0     53.5; %city 9
        34.5  33.2 35.2 33.5 33.5  33.7 36.1 21.4 53.5 0] ;  %city 10

[x y] = size(P);
C=0;
for j = 1:x
    A=P(j,:);
    B=0;
.
.
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function [YY1 YY2] = selection(P1,B,p)
% P1 - population
% B - fitness value [1 x n]
% p - population size
%-------------------------------------------------------------------------
[r1 c1]=find(B==max(B));
Y1(1,:)=P1(max(c1),:); % Keep the best first
P1(max(c1),:) = []; % remove
Fn(1,1)=1/B(1,max(c1)); % best fitness value 
B(:,max(c1))=[]; % remove
.
.
.

clear all
clc
close all
% Population size
p =50;
% Number of pairs of chromosomes required for crossover
c=25; 
% Number of chromosomes required for mutation
m=25; 
% Total number of generations
tg=1000;
% Adaptive restart condition
s = 30; 

figure
title('Blue - Minimum         Red - Average');
xlabel('Generation')
ylabel('Total travel distance')
hold on

w=1;
P0=population(1);

while w <= tg
    P=population(p-1);
    P(p,:)=P0;
    for i=1:tg   
            P(p+1:p+c,:)=crossover(P,c);
            P(p+c+1:p+c+m,:)=mutation(P,m);
.
.
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Sorry! This is only a half of the code.