Lightweight optimization with local, global, population-based and sequential techniques across mixed search spaces

🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.

A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP

A Python implementation of the Ant Colony Optimization Meta-Heuristic

Matlab Module for Stock Market Prediction using Simple NN

Heuristic Optimization for Python

Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)

A Hyper-Heuristic framework

An Ant Colony Optimization algorithm for the Traveling Salesman Problem

Modular Java framework for meta-heuristic optimization

Exact and meta-heuristic algorithms for NP problems

Meta-heuristic algorithm for Multi-Trip Vehicle Routing Problem with Time Windows

Black Widow Optimization implemented in pure Python.

In this section, I share the Meta-Heuristic algorithm codes that I wrote myself

Archive of my older research papers on optimization

Fast and easy solver for a lot of Vehicle Routing constraints

📄 Official implementation regarding the paper "Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification".

Online selection hyper-heuristic with generic parameter control in low-level heuristics (meta-heuristic).

🐝 Nature-Inspired Optimization Applied to Deep Learning for ICMC/USP mini-course.

Heuristic Optimization for C#, C, Java and Matlab

Local searches for continuous optimization implemented in C#

Currently a prototype implementation of Pareto local search algorithm in preparation for an upcoming project

A comprehensive solution to optimize tourism packages, prices, resources, and budgets for startup companies. The project includes mathematical modeling, data analysis, optimization algorithms, database design, and AI-driven demand prediction.

It was developed by creating a hybrid structure with the techniques of K-nearest neighbor algorithm and metaheuristic search algorithms. SOS Algorithm was used as the Meta-Heuristic algorithm.

Résolution du problème du sac à dos multidimensionnel (Multidimensional Knapsack Problem) grâce à plusieurs approches méta-heuristiques : algorithme génétique, Variable Neighborhood Search (VNS), Variable Neighborhood Descent (VND), et opérateurs de voisinage (swap, flip)

📄 Official implementation regarding the paper "A Survey on Metaheuristic Approaches to Feature Selection".

Local Search Scheduling with Simulated Annealing

Portfolio optimization using heuristic and meta-heuristic algorithms (Beam Search, Simulated Annealing, Genetic Algorithm) to maximize Sharpe ratio and find optimal asset allocation strategies.