Crate metaheuristics_nature

A collection of nature-inspired metaheuristic algorithms.

use metaheuristics_nature::{Report, RGA, RGASetting, Setting, Solver, Task};

fn main() {
    let mut a = RGA::new(
        MyObj::new(),
        RGASetting::default().task(Task::MinFit(1e-20)),
    );
    let ans = a.run();  // Run and get the final result
    let (x, y): (Vec<f64>, f64) = a.result();  // Get the optimized XY value of your function
    let reports: Vec<Report> = a.history();  // Get the history reports.
}

Features

• cli: Enable progress bar for CLI.

Macros

maybe	Generate random boolean by positive factor.
rand	Generate random values between [0., 1.) or by range.
setting_builder	Define a data structure and its builder functions.

Structs

AlgorithmBase	The base class of algorithms. Please see Algorithm for more information.
DE	Differential Evolution type.
DESetting	Differential Evolution settings.
FA	Firefly Algorithm type.
FASetting	Firefly Algorithm settings.
PSO	Particle Swarm Optimization type.
PSOSetting	Particle Swarm Optimization settings.
RGA	Real-coded Genetic Algorithm type.
RGASetting	Real-coded Genetic Algorithm settings.
Report	The data of generation sampling.
Setting	Base settings.
TLBO	Teaching Learning Based Optimization type.

Enums

Strategy	The Differential Evolution strategy. Each strategy has different formula on recombination.
Task	The terminal condition of the algorithm setting.

Traits

Algorithm	The methods of the metaheuristic algorithms.
ObjFunc	The base of the objective function.
Solver	The public API for Algorithm.

Type Definitions

TLBOSetting

Teaching Learning Based Optimization settings. This is a type alias to Setting.