Crate metaheuristics [−] [src]
Find approximate solutions to your optimisation problem using metaheuristics algorithms
What are Metaheuristics
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.
How can I use this crate
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
Problem crate.
Example
let solution = metaheuristics::hill_climbing::solve(&mut problem, runtime);
Modules
hill_climbing |
Find an approximate solution to your optimisation problem using Hill Climbing |
random_search |
Find an approximate solution to your optimisation problem using Random Search |
simulated_annealing |
Find an approximate solution to your optimisation problem using Simulated Annealing |
Traits
Metaheuristics |
Implement this simple trait to apply metaheuristics to your optimisation problems |