bdtsp_ga_basic(popS​ize, numIter, xy, alpha, range )

Optimizes Hub-spoke (number hubs, location of dynamic hubs) for blimp-drone delivery (see Amazon)

% FUNCTION: mclust_ga_basic calculates the minimum of the sum of the max
% distances for several clusters. In essence, it is a clustering tool
% that minimizes the max distances between each cluster and the farthest
% member of that cluster. Genetic algorithm minimizes the sum of the max
% distance of each of the centroid (cluster centers) then calculates the
% TSP distance from each centroid to each centroid.
% Inputs:
% population size and iterations
% xy coordinates
% speed of drone as a factor of blimp speed
% range of drone
% Outputs:
% number of centroids
% assignement of xy coordinates to centroids
% (note that one or more coordinates will become centroids)
% GA uses uses a tournament approach mutation type genetic algorithm.
% Initialize:
% (1) Calculate the distance from each xy coordinate to every other xy
% coordinate as distance matrix dmat.
% Body:
% (1) Randomly generate populations
% (2) Find min-cost of all pop (trials); keep best pop member and plot.
% (3) Shuffle (reshuffle) pop for a new tournament
% (4) Sub-group pop into groups of 4.
% Find the best of the 4; Overwrite worst of 4 from sub-group pop
% (5) Mutate the best of 4 (winner) in each sub-group
% (6) Insert best of 4 (winner) and all mutations back into population
% (7) If iteration budget remains, go to step 2, else terminate.
% Termination: based on iteration budget.
%
% Example of inputs:
% nStops=30; % Number of delivery stops for blimp-drone
% popSize = 500; % Size of the population of trials.
% numIter = 2500; % Number of iterations of GA; iteration budget.
% alpha = 2; % Speed of drone as a factor of blimp
% range = 10; % Range of drone (i.e. 10 km)
% xy=50*rand([nStops,2]);
% bdtsp_ga_basic(popSize, numIter, xy, alpha, range )