Common interface to all TSP solvers in this package.
Usage
solve_TSP(x, method = NULL, control = NULL, ...)
Arguments
x
the TSP given as an object of class TSP, ATSP
or ETSP.
method
method to solve the TSP (default: arbitrary insertion
algorithm with two_opt refinement.
control
a list of arguments passed on to the TSP solver
selected by method.
...
additional arguments are added to control.
Details
Treatment of NAs and infinite values in x:
TSP and ATSP contain distances and NAs are not allowed.
Inf is allowed and can be used to model the missing edges in
incomplete graphs
(i.e., the distance between the two objects is infinite).
Internally, Inf is replaced by a large value given by
max(x) + 2 range(x).
Note that the solution might still place the two objects next to each other
(e.g., if x contains several unconnected subgraphs)
which results in a path length of Inf.
All heuristics can be used with the control arguments repetitions
(uses the best from that many repetitions with random starts)
and two_opt (a logical indicating if two_opt refinement should be
performed). If several repetitions are done (this includes method "repetitive_nn") then foreach is used so they
can be performed in parallel on multiple cores/machines.
To enable parallel execution an appropriate parallel backend needs to be registered (e.g., load doParallel and register it with
registerDoParallel()).
ETSP are currently solved by first calculating a dissimilarity matrix (a
TSP). Only concorde and linkern can solve the TSP directly on the
ETSP.
Currently the following methods are available:
"identity", "random"
return a tour representing the order
in the data (identity order) or a random order.
Nearest,
farthest, cheapest and arbitrary insertion algorithms for a symmetric and
asymmetric TSP (Rosenkrantz et al. 1977).
The distances between cities are stored in a distance matrix D with
elements d(i,j). All insertion algorithms start with a tour
consisting of an arbitrary city and choose in each step a city k not
yet on the tour. This city is inserted into the existing tour between two
consecutive cities i and j, such that
d(i,k) + d(k,j) -
d(i,j)
is minimized. The algorithms stops when all cities are on the tour.
The nearest insertion algorithm chooses city k in each step as the
city which is nearest to a city on the tour.
For farthest insertion, the city k is chosen in each step as the city
which is farthest to any city on the tour.
Cheapest insertion chooses the city k such that the cost of inserting
the new city (i.e., the increase in the tour's length) is minimal.
Arbitrary insertion chooses the city k randomly from all cities not
yet on the tour.
Nearest and cheapest insertion tries to build the tour using cities which
fit well into the partial tour constructed so far. The idea behind behind
farthest insertion is to link cities far away into the tour fist to
establish an outline of the whole tour early.
Additional control options:
start
index of the
first city (default: random city).
"nn", "repetitive_nn"
Nearest neighbor and repetitive nearest neighbor algorithms for
symmetric and asymmetric TSPs (Rosenkrantz et al. 1977).
The algorithm starts with a tour containing a random city. Then the
algorithm always adds to the last city on the tour the nearest not yet
visited city. The algorithm stops when all cities are on the tour.
Repetitive nearest neighbor constructs a nearest neighbor tour for each
city as the starting point and returns the shortest tour found.
Additional control options:
start
index of the first city (default: random city).
"two_opt"
Two edge exchange improvement procedure (Croes 1958).
This is a tour refinement procedure which systematically exchanges two
edges in the graph represented
by the distance matrix till no improvements are possible. Exchanging two
edges is equal to reversing part of the tour. The resulting tour is called
2-optimal.
This method can be applied to tours created by other methods or used as
its own method. In this case improvement starts with a random tour.
Additional control options:
tour
an existing tour which should be improved.
If no tour is given, a random tour is used.
two_opt_repetitions
number of times to try
two_opt with a different initial
random tour (default: 1).
"concorde"
Concorde algorithm (Applegate et al. 2001).
Concorde is an advanced exact TSP solver for only symmetric TSPs
based on branch-and-cut. The program is not included in this package and
has to be obtained and installed separately (see
Concorde).
Additional control options:
exe
a character string containing the path to the executable
(see Concorde).
clo
a character string containing command line options for
Concorde, e.g., control = list(clo = "-B -v"). See
concorde_help on how to obtain a complete list of available
command line options.
precision
an integer which controls the number of
decimal places used for the internal representation of distances in
Concorde. The values given in x are multiplied by
10^{precision} before being passed on to Concorde. Note that
therefore the results produced by Concorde (especially lower and upper
bounds) need to be divided by 10^{precision} (i.e., the decimal point
has to be shifted precision placed to the left). The
interface to Concorde uses write_TSPLIB (see there for more
information).
"linkern"
Concorde's Chained Lin-Kernighan heuristic
(Applegate et al. 2003).
The Lin-Kernighan (Lin and Kernighan 1973)
heuristic uses variable k edge exchanges to
improve an initial tour. The program is not included in this package and
has to be obtained and installed separately (see
Concorde).
David Appletgate, Robert Bixby, Vasek Chvatal, William Cook (2001): TSP cuts
which do not conform to the template paradigm, Computational Combinatorial
Optimization, M. Junger and D. Naddef (editors), Springer.
D. Applegate, W. Cook and A. Rohe (2003): Chained Lin-Kernighan for Large
Traveling Salesman Problems. INFORMS Journal on Computing,
15(1):82–92.
G.A. Croes (1958): A method for solving traveling-salesman problems.
Operations Research, 6(6):791–812.
S. Lin and B. Kernighan (1973): An effective heuristic algorithm for the
traveling-salesman problem. Operations Research, 21(2): 498–516.
D.J. Rosenkrantz, R. E. Stearns, and Philip M. Lewis II (1977): An analysis of
several heuristics for the traveling salesman problem. SIAM Journal on
Computing, 6(3):563–581.
See Also
TOUR,
TSP,
ATSP,
write_TSPLIB,
Concorde.
Examples
## solve a simple Euclidean TSP (using the default method)
etsp <- ETSP(data.frame(x = runif(20), y = runif(20)))
tour <- solve_TSP(etsp)
tour
tour_length(tour)
plot(etsp, tour)
## compare methods
data("USCA50")
USCA50
methods <- c("identity", "random", "nearest_insertion",
"cheapest_insertion", "farthest_insertion", "arbitrary_insertion",
"nn", "repetitive_nn", "two_opt")
## calculate tours
tours <- lapply(methods, FUN = function(m) solve_TSP(USCA50, method = m))
names(tours) <- methods
## use the external solver which has to be installed separately
## Not run:
tours$concorde <- solve_TSP(USCA50, method = "concorde")
tours$linkern <- solve_TSP(USCA50, method = "linkern")
## End(Not run)
## register a parallel backend to perform repetitions in parallel
## Not run:
library(doParallel)
registerDoParallel()
## End(Not run)
## add some tours using repetition and two_opt refinements
tours$'nn+two_opt' <- solve_TSP(USCA50, method="nn", two_opt=TRUE)
tours$'nn+rep_10' <- solve_TSP(USCA50, method="nn", rep=10)
tours$'nn+two_opt+rep_10' <- solve_TSP(USCA50, method="nn", two_opt=TRUE, rep=10)
tours$'arbitrary_insertion+two_opt' <- solve_TSP(USCA50)
## show first tour
tours[[1]]
## compare tour lengths
opt <- 14497 # obtained by Concorde
tour_lengths <- c(sort(sapply(tours, tour_length), decreasing = TRUE),
optimal = opt)
dotchart(tour_lengths/opt*100-100, xlab = "percent excess over optimum")