metaheuropt 0.1.0

A lightweight Python library for metaheuristic optimization.

MetaHeurOpt

Description

MetaHeurOpt is a modular Python library designed for the execution, benchmarking, and statistical analysis of metaheuristic optimization algorithms. It provides a standardized framework to compare diverse algorithms, ranging from classic evolutionary strategies to modern physics-inspired and swarm intelligence models, on complex continuous optimization problems.

Installation

You can install the stable version of MetaHeurOpt directly from PyPI:

pip install metaheuropt

Usage Example

Below is a quick start guide to comparing solvers. For a more detailed walkthrough, including statistical tables and 3D landscape visualization, check out the /examples/example.ipynb notebook.

import numpy as np
from metaheuropt.solvers import GA, PSO
from metaheuropt.core import Optimizer
from metaheuropt.analysis import ResultsAnalyzer

# 1. Define your objective function
def sphere(x):
    return np.sum(np.square(x))

# 2. Configure experiment
dims = 10
bounds = (np.array([-5.12] * dims), np.array([5.12] * dims))
num_runs = 5
max_iter = 100

# 3. Select solvers
solvers = [
    GA(bounds=bounds, pop_size=50, max_iter=max_iter),
    PSO(bounds=bounds, pop_size=50, max_iter=max_iter),
]

# 4. Run optimization
opt = Optimizer(solvers=solvers, obj_func=sphere, num_runs=num_runs)
opt.run(save_results=True, results_folder="demo_results")

# 5. Analyze and visualize
analyzer = ResultsAnalyzer("demo_results")
analyzer.plot_convergence()
analyzer.perform_stats(full=True)

Available Algorithms

The library currently supports the following high-performance metaheuristics:

Code	Name	Original Reference
ABC	Artificial Bee Colony	Karaboga (2007)
CMA-ES	Covariance Matrix Adaptation Evolution Strategy	Hansen & Ostermeier (2001)
DE	Differential Evolution	Storn & Price (1997)
GA	Genetic Algorithm	K. F. Man et al. (1976)
GSA	Gravitational Search Algorithm	Rashedi et al. (2009)
GWO	Grey Wolf Optimizer	Mirjalili et al. (2014)
HHO	Harris Hawks Optimization	Heidari et al. (2019)
JADE	Adaptive Differential Evolution with Archive	Zhang & Sanderson (2009)
MVO	Multi-Verse Optimizer	Mirjalili et al. (2016)
NSGA-II	Non-dominated Sorting Genetic Algorithm II	Deb et al. (2002)
PSO	Particle Swarm Optimization	Kennedy & Eberhart (1995)
SA	Simulated Annealing	Kirkpatrick et al. (1983)
TLBO	Teaching-Learning-Based Optimization	Rao et al. (2011)
WOA	Whale Optimization Algorithm	Mirjalili & Lewis (2016)