Library for traveling salesman problem algorithms.
For 2d point datasets:
use std::time; use tsp_rs::Tour; use tsp_rs::point::Point; let tour: Vec<Point> = vec![ Point::new(0., 0.), Point::new(0., 1.), Point::new(1., 0.), Point::new(1., 1.), ]; let mut tour = Tour::from(&tour); tour.optimize_kopt(std::time::Duration::from_secs(1));
Same as above, but instead of using
tsp::point::Point, just implement the trait
for your type
T by defining a distance function between two
T. Your type will also need
Borrow, maybe another.. the compiler will remember.
Path::solve_kopt uses a 2-opt heuristic with 3-opt thrown in if it hits a wall for too long. Gets to within ~8% of the optimal solution for the b52 and ~10% of qa194 on average in a run of solve_nn + 1 second of optimization. The larger the problem, the longer you should allow for optimization.
For the constructive solution,
Path::solve_nn, gets to within ~15% of the optimal solution on average.
Just for my own entertainment while learning rust, don't trust this but the implementation should be correct.