Samyama Optimization Engine (Rust)

A high-performance library implementing metaphor-less metaheuristic optimization algorithms.

Algorithms

• Jaya: Parameter-less optimization (Toward best, away from worst).
• Rao (1, 2, 3): Metaphor-less algorithms using best, worst, and mean solutions.
• TLBO / ITLBO: Teaching-Learning-Based Optimization (with Elitism in ITLBO).
• QOJaya: Quasi-Oppositional Jaya (using Opposition-Based Learning).
• BMR / BWR: Best-Mean-Random and Best-Worst-Random strategies.
• PSO: Particle Swarm Optimization.
• DE: Differential Evolution.
• GA: Genetic Algorithm (Tournament selection, Uniform crossover).
• GWO: Grey Wolf Optimizer (Alpha, Beta, Delta hierarchy).
• Firefly: Firefly Algorithm (Light intensity based attraction).
• Cuckoo: Cuckoo Search (Levy flights and nest abandonment).
• SA: Simulated Annealing.
• Bat: Bat Algorithm (Echolocation-based search).
• ABC: Artificial Bee Colony.

Features

• Parallel Evaluation: Automatic multi-threaded fitness calculation via rayon.
• Zero-Copy: Minimal overhead when operating on large vectors.
• Python Bindings: Easy integration with Python data science stacks.

Usage (Rust)

use samyama_optimization::algorithms::*;
use samyama_optimization::common::*;
use ndarray::array;

let problem = SimpleProblem {
    objective_func: |x| x.iter().map(|&v| v * v).sum(),
    dim: 2,
    lower: array![-10.0, -10.0],
    upper: array![10.0, 10.0],
};

let solver = JayaSolver::new(SolverConfig::default());
let result = solver.solve(&problem);
println!("Best: {:?}", result.best_variables);

Usage (Python)

import samyama_optimization as so
import numpy as np

def sphere(x):
    return np.sum(x**2)

res = so.solve_jaya(sphere, np.array([-10, -10]), np.array([10, 10]))
print(f"Best fitness: {res.best_fitness}")