fmqa / simulatedannealing

A simulated annealing optimization library

Simulated Annealing

A library for solving optimization problems by simulated annealing, based on pathfinder's original implementation.

Install

Using jitpack.io

<repositories>
  <repository>
    <id>jitpack.io</id>
    <url>https://jitpack.io</url>
  </repository>
</repositories>
<dependency>
  <groupId>com.github.kochab</groupId>
  <artifactId>simulatedannealing</artifactId>
  <version>0.0.19</version>
</dependency>

Usage

You will need to implement the SearchState<T> interface. It defines only one method, T step(), which should return a randomly chosen perturbation of 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();

Examples

See kochab/simulatedannealing-examples for a simple demo program that uses simulated annealing to solve a graphical optimization problem (minimizing adjacent difference between pixels).

LICENSE

MIT.