Truck, Multi-drone Evolutionary Routing Prob软件

Evolutionary Genetic Algorithm to ~optimally route the Truck-Multiple Drone Configuration

软件应用简介

Truck, Multi-drone Evolutionary Routing Prob软件

Evolutionary Genetic Algorithm to ~optimally route the Truck-Multiple Drone Configuration

FUNCTION: dvtsp_ga_basic calculates the minimum delivery time for a truck and

% multiple (nu) drones to deliver n parcels where each drone is capable of

% working in tandem with the truck to deliver only one parcel.

% System parameters for the drones are:

% kappa = drone range,

% alpha = drone speed as a factor of truck speed (1)

% nu = number of drones assigned to assist the truck.

% Given a graph G(V,E) with vertices V and edges E, the algorithm routes

% the truck ~optimally by assigning drones while routing the truck.

% For any operation (launch, delivery, rendezvous), one or up to nu drones

% may be assigned deliveries within range such that they can rendezvous

% back with the truck at the truck’s next delivery location.

% Inputs:

% population size

% number of iterations

% xy coordinates

% alpha: max speed of drone as a factor of truck speed

% kappa: max range of drone (i.e. km)

% nu: max number of drones assigned to a truck

% Outputs:

% minimum ~optimal time based on distance with speed=1

% truck route

% drone route

%

% Evolutionary Algorithm (EA) uses uses a tournament mutation approach.

% Example of inputs:

% nStops = 20; % Number of delivery stops for truck-and-drones

% popSize = 500; % Size of the population of trials for mutation.

% numIter = 3500; % Number of iterations of GA; iteration budget.

% alpha = 2; % Speed of drone as a factor of truck

% kappa = 10; % Range of drone (i.e. 10 km)

% nu = 4; % Max number of drones assigned to truck

% xy = 50*rand([nStops,2]); % coordinates of stops