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Multiple Variable Traveling Salesmen Problem - Genetic Algorithm

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Multiple Variable Traveling Salesmen Problem - Genetic Algorithm

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02 Sep 2008 (Updated )

Finds a near-optimal solution to a variation of the MTSP with variable number of salesmen using a GA

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Description

MTSPV_GA Variable Multiple Traveling Salesmen Problem (M-TSP) Genetic Algorithm (GA)
  Finds a (near) optimal solution to a variation of the M-TSP (that has a
  variable number of salesmen) by setting up a GA to search for the
  shortest route (least distance needed for the salesmen to travel to
  each city exactly once and return to their starting locations)
Summary:
1. Each salesman travels to a unique set of cities and completes the
   route by returning to the city he started from
2. Each city is visited by exactly one salesman

Input:
USERCONFIG (structure) with zero or more of the following fields:
- XY (float) is an Nx2 matrix of city locations, where N is the number of cities
- DMAT (float) is an NxN matrix of point to point distances or costs
- MINTOUR (scalar integer) is the minimum tour length for any of the salesmen
- POPSIZE (scalar integer) is the size of the population (should be divisible by 4)
- NUMITER (scalar integer) is the number of desired iterations for the algorithm to run
- SHOWPROG (scalar logical) shows the GA progress if true
- SHOWRESULT (scalar logical) shows the GA results if true
- SHOWWAITBAR (scalar logical) shows a waitbar if true

Input Notes:
1. Rather than passing in a structure containing these fields, any/all of
   these inputs can be passed in as parameter/value pairs in any order instead.
2. Field/parameter names are case insensitive but must match exactly otherwise.

Output:
RESULTSTRUCT (structure) with the following fields:
    (in addition to a record of the algorithm configuration)
- OPTROUTE (integer array) is the best route found by the algorithm
- OPTBREAK (integer array) is the list of route break points (these specify the indices
    into the route used to obtain the individual salesman routes)
- MINDIST (scalar float) is the total distance traveled by the salesmen

Route/Breakpoint Details:
If there are 10 cities and 3 salesmen, a possible route/break
combination might be: rte = [5 6 9 1 4 2 8 10 3 7], brks = [3 7]
Taken together, these represent the solution [5 6 9][1 4 2 8][10 3 7],
which designates the routes for the 3 salesmen as follows:
    . Salesman 1 travels from city 5 to 6 to 9 and back to 5
    . Salesman 2 travels from city 1 to 4 to 2 to 8 and back to 1
    . Salesman 3 travels from city 10 to 3 to 7 and back to 10

Usage:
mtspv_ga
  -or-
mtspv_ga(userConfig)
  -or-
resultStruct = mtspv_ga;
  -or-
resultStruct = mtspv_ga(userConfig);
  -or-
[...] = mtspv_ga('Param1',Value1,'Param2',Value2, ...);

Example:
% Let the function create an example problem to solve
mtspv_ga;

Example:
% Request the output structure from the solver
resultStruct = mtspv_ga;

Example:
% Pass a random set of user-defined XY points to the solver
userConfig = struct('xy',10*rand(35,2));
resultStruct = mtspv_ga(userConfig);

Example:
% Pass a more interesting set of XY points to the solver
n = 50;
phi = (sqrt(5)-1)/2;
theta = 2*pi*phi*(0:n-1);
rho = (1:n).^phi;
[x,y] = pol2cart(theta(:),rho(:));
xy = 10*([x y]-min([x;y]))/(max([x;y])-min([x;y]));
userConfig = struct('xy',xy);
resultStruct = mtspv_ga(userConfig);

Example:
% Pass a random set of 3D (XYZ) points to the solver
xyz = 10*rand(35,3);
userConfig = struct('xy',xyz);
resultStruct = mtspv_ga(userConfig);

Example:
% Change the defaults for GA population size and number of iterations
userConfig = struct('popSize',200,'numIter',1e4);
resultStruct = mtspv_ga(userConfig);

Example:
% Turn off the plots but show a waitbar
userConfig = struct('showProg',false,'showResult',false,'showWaitbar',true);
resultStruct = mtspv_ga(userConfig);

Acknowledgements

This file inspired Mdmtspv Ga Multiple Depot Multiple Traveling Salesmen Problem Solved By Genetic Algorithm.

Required Products MATLAB
MATLAB release MATLAB 8.3 (R2014a)
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Comments and Ratings (3)
24 Nov 2012 Bharath

Could someone tell me where can I get the code for solving the same MTSP using ACO in MATLAB?

22 Oct 2012 Emmanuel Luevano

Hello, really great job!!!

Could you help me to change this example into simulink, please!

01 Oct 2008 The Author

Update: The SINGLES parameter has been replaced with a more generalized MIN_TOUR.

Updates
02 Jun 2009

Added 3D capability.

07 Nov 2011

Minor cosmetic updates.

06 May 2014

Major overhaul of input/output interface.

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