MTSPV_GA Variable Multiple Traveling Salesmen Problem (MTSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to a variation of the MTSP (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 userdefined 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:n1);
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);
