A simple framework for solving optimization problems by simulated annealing.
Pathfinder solves the k-Vehicle Routing Problem using simulated annealing at https://github.com/csse497/pathfinder-routing. We couldn't find any existing frameworks we liked and it was simple enough to implement, so we created our own.
Available on Maven.
<dependency>
<groupId>xyz.thepathfinder</groupId>
<artifactId>simulatedannealing</artifactId>
<version>0.0.3</version>
</dependency>You will need to implement the SearchState<T> interface. It defines only one method, T step(), which should return a randomly chosen deviation from the current state. Also, you will need to implement the Problem<T> interface, which initializes a state and evaluates the energey of states.
You will need to choose a Scheduler implementation, which determines the speed and shape of the annealing process. We provide two built-in options, LinearDecayScheduler and ExponentialDecayScheduler.
Finally, just create a Solver and call solve!
Scheduler scheduler = new LinearDecayScheduler(INITIAL_TEMPERATURE, NUMBER_OF_STEPS);
Problem<VRPSearchState> problem = new VehicleRoutingProblem(...);
Solver<VRPSearchState> solver = new Solver(problem, scheduler);
VRPSearchState solution = solver.solve();MIT.