Find approximate solutions to your optimisation problem using metaheuristics algorithms
The aim of this crate is to host various Metaheuristics algorithms. Patches implementing useful algorithms most welcome.
The documentation for this crate can be found here.
Metaheuristics are a class of stochastic optimisation algorithms. These type of algorithms rely on randomness to jump around the search space, then sample where they land for possible solutions. In simple terms, metaheuristics are structured trial and error.
If you've got a trial and error problem, and individual trials can be compared and ranked against each other, Metaheuristics may be your most viable option at getting good results.
By implementing the
Metaheuristics trait, the algorithms within the following modules will be
available to you. To see an example implementation, check out the Travelling Salesman
let solution = metaheuristics::hill_climbing::solve(&mut problem, runtime);
Please report any bugs or feature requests at:
Feel free to fork the repository and submit pull requests :)
IT COMES WITHOUT WARRANTY OF ANY KIND.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
Find an approximate solution to your optimisation problem using Hill Climbing
Find an approximate solution to your optimisation problem using Random Search
Find an approximate solution to your optimisation problem using Simulated Annealing
Implement this simple trait to apply metaheuristics to your optimisation problems