The example below can get you started. In order to use it in your own optimization problem, you will need to define an objective function as it is defined in the run function, and a Config object. See the documentation for more information.

Example

use pso_rs::*;

// define objective function (d-dimensional Rosenbrock)
fn objective_function(
    p: &Particle,
    _flat_dim: usize,
    dimensions: &Vec<usize>
) -> f64 {
    (0..dimensions[0] - 1).map(|i| {
        100.0 * ((p[i+1]-p[i]).powf(2.0)).powf(2.0)
            + (1.0-p[i]).powf(2.0)
    }).sum()
}

// define a termination condition (optional)
fn terminate(f_best: f64) -> bool {
    f_best - (0.0) < 1e-4
}

let config = Config {
    // dimension shape of each particle
    dimensions: vec![2],
    // problem bounds in each dimension
    bounds: vec![(-5.0, 10.0); 2],
    // maximum no. of objective function computations
    t_max: 10000,
    // leave the rest of the params as default
    ..Config::default()
};

let pso = pso_rs::run(
    config,
    objective_function,
    Some(terminate)
).unwrap();

let model = pso.model;
println!("Model: {:?} ", model.get_f_best());