Solution to a travelling salesman problem in genetic algorithm

hello experts.

I have aproblem in using optimtool to solve a travelling salesman problem while changing parameters and also changing the no. of cities after 10 runs

the code is below

% Custom Data Type Optimization Using the Genetic Algorithm

% This example shows how to use the genetic algorithm to minimize a

% function using a custom data type. The genetic algorithm is customized to

% solve the traveling salesman problem.

% Copyright 2004-2015 The MathWorks, Inc.

%% Traveling Salesman Problem

% The traveling salesman problem is an optimization problem where there is

% a finite number of cities, and the cost of travel between each city is

% known. The goal is to find an ordered set of all the cities for the

% salesman to visit such that the cost is minimized. To solve the traveling

% salesman problem, we need a list of city locations and distances, or

% cost, between each of them.

%%

% Our salesman is visiting cities in the United States. The file

% |usborder.mat| contains a map of the United States in the variables |x|

% and |y|, and a geometrically simplified version of the same map in the

% variables |xx| and |yy|.

load('usborder.mat','x','y','xx','yy');

plot(x,y,'Color','red'); hold on;

%%

% We will generate random locations of cities inside the border of the

% United States. We can use the |inpolygon| function to make sure that all

% the cities are inside or very close to the US boundary.

cities = 200;

locations = zeros(cities,2);

n = 1;

while (n <= cities)

xp = rand*1.5;

yp = rand;

if inpolygon(xp,yp,xx,yy)

locations(n,1) = xp;

locations(n,2) = yp;

n = n+1;

end

end

plot(locations(:,1),locations(:,2),'bo');

%%

% Blue circles represent the locations of the cities where the salesman

% needs to travel and deliver or pickup goods. Given the list of city

% locations, we can calculate the distance matrix for all the cities.

distances = zeros(cities);

for count1=1:cities,

for count2=1:count1,

x1 = locations(count1,1);

y1 = locations(count1,2);

x2 = locations(count2,1);

y2 = locations(count2,2);

distances(count1,count2)=sqrt((x1-x2)^2+(y1-y2)^2);

distances(count2,count1)=distances(count1,count2);

end;

end;

%% Customizing the Genetic Algorithm for a Custom Data Type

% By default, the genetic algorithm solver solves optimization problems

% based on double and binary string data types. The functions for creation,

% crossover, and mutation assume the population is a matrix of type double,

% or logical in the case of binary strings. The genetic algorithm solver

% can also work on optimization problems involving arbitrary data types.

% You can use any data structure you like for your population. For example,

% a custom data type can be specified using a MATLAB(R) cell array. In order

% to use |ga| with a population of type cell array you must provide a

% creation function, a crossover function, and a mutation function that

% will work on your data type, e.g., a cell array.

%% Required Functions for the Traveling Salesman Problem

% This section shows how to create and register the three required

% functions. An individual in the population for the traveling salesman

% problem is an ordered set, and so the population can easily be

% represented using a cell array. The custom creation function for the

% traveling salesman problem will create a cell array, say |P|, where each

% element represents an ordered set of cities as a permutation vector. That

% is, the salesman will travel in the order specified in |P{i}|. The creation

% function wopill return a cell array of size |PopulationSize|.

type create_permutations.m

%%

% The custom crossover function takes a cell array, the population, and

% returns a cell array, the children that result from the crossover.

type crossover_permutation.m

%%

% The custom mutation function takes an individual, which is an ordered set

% of cities, and returns a mutated ordered set.

type mutate_permutation.m

%%

% We also need a fitness function for the traveling salesman problem. The

% fitness of an individual is the total distance traveled for an ordered

% set of cities. The fitness function also needs the distance matrix to

% calculate the total distance.

type traveling_salesman_fitness.m

%%

% |ga| will call our fitness function with just one argument |x|, but our

% fitness function has two arguments: |x|, |distances|. We can use an

% anonymous function to capture the values of the additional argument, the

% distances matrix. We create a function handle |FitnessFcn| to an

% anonymous function that takes one input |x|, but calls

% |traveling_salesman_fitness| with |x|, and distances. The variable,

% distances has a value when the function handle |FitnessFcn| is created,

% so these values are captured by the anonymous function.

%distances defined earlier

FitnessFcn = @(x) traveling_salesman_fitness(x,distances);

%%

% We can add a custom plot function to plot the location of the cities and

% the current best route. A red circle represents a city and the blue

% lines represent a valid path between two cities.

type traveling_salesman_plot.m

%%

% Once again we will use an anonymous function to create a function handle

% to an anonymous function which calls |traveling_salesman_plot| with the

% additional argument |locations|.

%locations defined earlier

my_plot = @(options,state,flag) traveling_salesman_plot(options, ...

state,flag,locations);

%% Genetic Algorithm Options Setup

% First, we will create an options container to indicate a custom data type

% and the population range.

options = optimoptions(@ga, 'PopulationType', 'custom','InitialPopulationRange', ...

[1;cities]);

%%

% We choose the custom creation, crossover, mutation, and plot functions

% that we have created, as well as setting some stopping conditions.

options = optimoptions(options,'CreationFcn',@create_permutations, ...

'CrossoverFcn',@crossover_permutation, ...

'MutationFcn',@mutate_permutation, ...

'PlotFcn', my_plot, ...

'MaxGenerations',500,'PopulationSize',60, ...

'MaxStallGenerations',200,'UseVectorized',true);

%%

% Finally, we call the genetic algorithm with our problem information.

numberOfVariables = cities;

[x,fval,reason,output] = ...

ga(FitnessFcn,numberOfVariables,[],[],[],[],[],[],[],options)

%%

% The plot shows the location of the cities in blue circles as well as the

% path found by the genetic algorithm that the salesman will travel. The

% salesman can start at either end of the route and at the end, return to

% the starting city to get back home.

SOMEONE TO HELP ME UNDERSTAND HOW TO PUT CONSTRAINTS PARAMETER AND ENABLING THE OPTIMUM TOOL RUN SMOOTHLY

[Cris LaPierre]

Do any of these answers help?

[Martin Njagi]

I've gone through them but non is explaining how to solve it in the optimization tool interface