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Open Multiple Traveling Salesmen Problem - Genetic Algorithm

version 1.3.0.0 (13.6 KB) by Joseph Kirk

Joseph Kirk (view profile)

Finds a near-optimal solution to a "open" variation of the M-TSP using a GA

Updated 06 May 2014

MTSPO_GA Open Multiple Traveling Salesmen Problem (M-TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to a variation of the M-TSP by setting
up a GA to search for the shortest route (least distance needed for the
salesmen to travel to each city exactly once without returning to their
starting location)
Summary:
1. Each salesman travels to a unique set of cities (although none of
them close their loops by returning to their starting points)
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 city-to-city distances or costs
- NSALESMEN (scalar integer) is the number of salesmen to visit the cities
- 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 8)
- 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
. Salesman 2 travels from city 1 to 4 to 2 to 8
. Salesman 3 travels from city 10 to 3 to 7

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

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

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

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

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

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

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

Comments and Ratings (1)

The Author

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