metaheuristics-nature

A collection of nature-inspired metaheuristic algorithms. Provides objective function trait, well-known methods, and tool functions let you implement your own searching method.

This crate implemented following algorithms:

• Real-coded Genetic Algorithm (RGA)
• Differential Evolution (DE)
• Particle Swarm Optimization (PSO)
• Firefly Algorithm (FA)
• Teaching-Learning Based Optimization (TLBO)

Each algorithm gives same API and default parameters to help you test different implementation. For example, you can test another algorithm by simply replacing Rga to De.

use metaheuristics_nature::{Rga, Solver, Task};

let a = Solver::solve(
    MyFunc::new(),
    Rga::default().task(Task::MinFit(1e-20)),
    |_| true // Run without callback
);
// Get the result from objective function
let ans = a.result();
// Get the optimized XY value of your function
let (x, y) = a.parameters();
// Get the history reports
let history = a.history();