Struct metaheuristics_nature::Solver

pub struct Solver<F: ObjFunc, R> { /* private fields */ }

A public API for using optimization methods.

Users can simply obtain their solution and see the result.

• The method is a type that implemented Algorithm.
• The objective function is a type that implement ObjFunc.
• A basic algorithm data is hold by Context.

The builder of this type can infer the algorithm by Setting::Algorithm.

use metaheuristics_nature::{Rga, Solver};

// Build and run the solver
let s = Solver::build(Rga::default())
    .task(|ctx| ctx.gen == 20)
    .solve(MyFunc::new());
// Get the result from objective function
let ans = s.result();
// Get the optimized XY value of your function
let x = s.best_parameters();
let y = s.best_fitness();
// Get the history reports
let report = s.report();

Implementations

impl<F: ObjFunc, R> Solver<F, R>

pub fn func(&self) -> &F

Get the reference of the objective function.

It’s useful when you need to get the preprocessed data from the initialization process, which is stored in the objective function.

pub fn report(&self) -> &[R]

Get the history report returned by record function.

pub fn best_parameters(&self) -> &[f64]

Get the best parameters.

pub fn best_fitness(&self) -> F::Fitness

Get the best fitness.

pub fn result(&self) -> F::Result

Get the result of the objective function.

pub fn seed(&self) -> u128

Seed of the random number generator.

impl<F: ObjFunc> Solver<F, ()>

pub fn build<S>(setting: S) -> SolverBuilder<'static, S, F, ()> where
    S: Setting, 

Start to build a solver. Take a setting and setup the configurations.

Please check SolverBuilder type, it will help you choose your configuration.

If all things are well-setup, call SolverBuilder::solve.

The default value of each option can be found in their document.